Signal processing for measurement of physiological analytes

ABSTRACT

A method is provided for continually or continuously measuring the concentration of target chemical analytes present in a biological system, and processing analyte-specific signals to obtain a measurement value that is closely correlated with the concentration of the target chemical analyte in the biological system. One important application of the invention involves a method for signal processing in a system for monitoring blood glucose values.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation, pursuant 35 U.S.C. §120, of U.S.patent application Ser. No. 09/794,783, filed Feb. 27, 2001, now U.S.Pat. No. 6,595,919, which is a continuation, pursuant 35 U.S.C. §120, ofU.S. patent application Ser. No. 09/309,728, filed May 11, 1999, nowU.S. Pat. No. 6,233,471, which claims priority, under 35 USC §119(e)(1),to provisional patent application Ser. No. 60/085,344, filed May 13,1998, and which applications are incorporated herein by reference withtheir entireties.

FIELD OF THE INVENTION

The invention relates generally to methods for continually orcontinuously measuring the concentration of target chemical analytespresent in a biological system. More particularly, the invention relatesto methods for processing signals obtained during measurement ofphysiological analytes. One important application of the inventioninvolves a method for monitoring blood glucose concentrations.

BACKGROUND OF THE INVENTION

A number of diagnostic tests are routinely performed on humans toevaluate the amount or existence of substances present in blood or otherbody fluids. These diagnostic tests typically rely on physiologicalfluid samples removed from a subject, either using a syringe or bypricking the skin. One particular diagnostic test entailsself-monitoring of blood glucose levels by diabetics.

Diabetes is a major health concern, and treatment of the more severeform of the condition, Type I (insulin-dependent) diabetes, requires oneor more insulin injections per day. Insulin controls utilization ofglucose or sugar in the blood and prevents hyperglycemia which, if leftuncorrected, can lead to ketosis. On the other hand, improperadministration of insulin therapy can result in hypoglycemic episodes,which can cause coma and death. Hyperglycemia in diabetics has beencorrelated with several long-term effects of diabetes, such as heartdisease, atherosclerosis, blindness, stroke, hypertension and kidneyfailure.

The value of frequent monitoring of blood glucose as a means to avoid orat least minimize the complications of Type I diabetes is wellestablished. Patients with Type II (non-insulin-dependent) diabetes canalso benefit from blood glucose monitoring in the control of theircondition by way of diet and exercise.

Conventional blood glucose monitoring methods generally require thedrawing of a blood sample (e.g., by fingerprick) for each test, and adetermination of the glucose level using an instrument that readsglucose concentrations by electrochemical or calorimetric methods. TypeI diabetics must obtain several fingerprick blood glucose measurementseach day in order to maintain tight glycemic control. However, the painand inconvenience associated with this blood sampling, along with thefear of hypoglycemia, has led to poor patient compliance, despite strongevidence that tight control dramatically reduces long-term diabeticcomplications. In fact, these considerations can often lead to anabatement of the monitoring process by the diabetic. See, e.g., TheDiabetes Control and Complications Trial Research Group (1993) New Engl.J. Med. 329:977-1036.

Recently, various methods for determining the concentration of bloodanalytes without drawing blood have been developed. For example, U.S.Pat. No. 5,267,152 to Yang et al. describes a noninvasive technique ofmeasuring blood glucose concentration using near-IR radiationdiffuse-reflection laser spectroscopy. Similar near-IR spectrometricdevices are also described in U.S. Pat. No. 5,086,229 to Rosenthal etal. and U.S. Pat. No. 4,975,581 to Robinson et al.

U.S. Pat. Nos. 5,139,023 to Stanley et al., and 5,443,080 to D'Angelo etal. describe transdermal blood glucose monitoring devices that rely on apermeability enhancer (e.g., a bile salt) to facilitate transdermalmovement of glucose along a concentration gradient established betweeninterstitial fluid and a receiving medium. U.S. Pat. No. 5,036,861 toSembrowich describes a passive glucose monitor that collectsperspiration through a skin patch, where a cholinergic agent is used tostimulate perspiration secretion from the eccrine sweat gland. Similarperspiration collection devices are described in U.S. Pat. No. 5,076,273to Schoendorfer and U.S. Pat. No. 5,140,985 to Schroeder.

In addition, U.S. Pat. No. 5,279,543 to Glikfeld et al. describes theuse of iontophoresis to noninvasively sample a substance through skininto a receptacle on the skin surface. Glikfeld teaches that thissampling procedure can be coupled with a glucose-specific biosensor orglucose-specific electrodes in order to monitor blood glucose. Finally,International Publication No. WO 96/00110, published Jan. 4, 1996,describes an iontophoretic apparatus for transdermal monitoring of atarget substance, wherein an iontophoretic electrode is used to move ananalyte into a collection reservoir and a biosensor is used to detectthe target analyte present in the reservoir.

SUMMARY OF THE INVENTION

The present invention provides a method for continually or continuouslymeasuring the concentration of an analyte present in a biologicalsystem. The method entails continually or continuously detecting ananalyte from the biological system and deriving a raw signal therefrom,wherein the raw signal is related to the analyte concentration. A numberof signal processing steps are then carried out in order to convert theraw signal into an initial signal output that is indicative of ananalyte amount. The converted signal is then further converted into avalue indicative of the concentration of analyte present in thebiological system.

The raw signal can be obtained using any suitable sensing methodologyincluding, for example, methods which rely on direct contact of asensing apparatus with the biological system; methods which extractsamples from the biological system by invasive, minimally invasive, andnon-invasive sampling techniques, wherein the sensing apparatus iscontacted with the extracted sample; methods which rely on indirectcontact of a sensing apparatus with the biological system; and the like.In preferred embodiments of the invention, methods are used to extractsamples from the biological sample using minimally invasive ornon-invasive sampling techniques. The sensing apparatus used with any ofthe above-noted methods can employ any suitable sensing element toprovide the raw signal including, but not limited to, physical,chemical, electrochemical, photochemical, spectrophotometric,polarimetric, colorimetric, radiometric, or like elements. In preferredembodiments of the invention, a biosensor is used which comprises anelectrochemical sensing element.

In one particular embodiment of the invention, the raw signal isobtained using a transdermal sampling system that is placed in operativecontact with a skin or mucosal surface of the biological system. Thesampling system transdermally extracts the analyte from the biologicalsystem using any appropriate sampling technique, for example,iontophoresis. The transdermal sampling system is maintained inoperative contact with the skin or mucosal surface of the biologicalsystem to provide for such continual or continuous analyte measurement.

The analyte can be any specific substance or component that one isdesirous of detecting and/or measuring in a chemical, physical,enzymatic, or optical analysis. Such analytes include, but are notlimited to, amino acids, enzyme substrates or products indicating adisease state or condition, other markers of disease states orconditions, drugs of abuse, therapeutic and/or pharmacologic agents,electrolytes, physiological analytes of interest (e.g., calcium,potassium, sodium, chloride, bicarbonate (CO₂), glucose, urea (bloodurea nitrogen), lactate, hematocrit, and hemoglobin), lipids, and thelike. In preferred embodiments, the analyte is a physiological analyteof interest, for example glucose, or a chemical that has a physiologicalaction, for example a drug or pharmacological agent.

Accordingly, it is an object of the invention to provide a method forcontinually or continuously measuring an analyte present in a biologicalsystem, wherein raw signals are obtained from a suitable sensingapparatus, and then subjected to signal processing is techniques. Moreparticularly, the raw signals undergo a data screening method in orderto eliminate outlier signals and/or poor (incorrect) signals using apredefined set of selection criteria. In addition, or alternatively, theraw signal can be converted in a conversion step which (i) removes orcorrects for background information, (ii) integrates the raw signal overa sensing time period, (iii) performs any process which converts the rawsignal from one signal type to another, or (iv) performs any combinationof steps (i), (ii) and/or (iii). In preferred embodiments, theconversion step entails a baseline background subtraction method toremove background from the raw signal and an integration step. In otherembodiments, the conversion step can be tailored for use with a sensingdevice that provides both active and reference (blank) signals; whereinmathematical transformations are used to individually smooth active andreference signals, and/or to subtract a weighted reference (blank)signal from the active signal. In still further embodiments, theconversion step includes correction functions which account for changingconditions in the biological system and/or the biosensor system (e.g.,temperature fluctuations in the biological system, temperaturefluctuations in the sensor element, skin conductivity fluctuations, orcombinations thereof). The result of the conversion step is an initialsignal output which provides a value which can be correlated with theconcentration of the target analyte in the biological sample.

It is also an object of the invention to provide a signal processingcalibration step, wherein the raw or initial signals obtained asdescribed above are converted into an analyte-specific value of knownunits to provide an interpretation of the signal obtained from thesensing device. The interpretation uses a mathematical transformation tomodel the relationship between a measured response in the sensing deviceand a corresponding analyte-specific value. Such mathematicaltransformations can entail the use of linear or nonlinear regressions,or neural network algorithms. In one embodiment, the calibration stepentails calibrating the sensing device using a single- or multi-pointcalibration, and then converting post-calibration data using correlationfactors, time corrections and constants to obtain an analyte-specificvalue. Further signal processing can be used to refine the informationobtained in the calibration step, for example, where a signal processingstep is used to correct for signal differences due to variableconditions unique to the sensor element used to obtain the raw signal.In one embodiment, this further step is used to correct for signaltime-dependence, particularly signal decline. In another embodiment, aconstant offset term is obtained, which offset is added to the signal toaccount for a non-zero signal at an estimated zero analyteconcentration.

Further, the methods of the present invention include enhancement ofskin permeability by pricking the skin with micro-needles. In addition,the sampling system can be programed to begin execution of sampling andsensing at a defined time(s).

It is yet a further object of the invention to provide a monitoringsystem for continually or continuously measuring an analyte present in abiological system. The monitoring system comprises, in operativecombination: (a) a sampling means for continually or continuouslyextracting the analyte from the biological system, (b) a sensing meansin operative contact with the analyte extracted by the sampling means,and (c) a microprocessor means in operative communication with thesensing means. The sampling means is adapted for extracting the analyteacross a skin or mucosal surface of a biological system. The sensingmeans is used to obtain a raw signal from the extracted analyte, whereinthe raw signal is specifically related to the analyte. Themicroprocessor means is used to subject the raw signal to a conversionstep, thereby converting the same into an initial signal output which isindicative of the amount of analyte extracted by the sampling means, andthen perform a calibration step which correlates the initial signaloutput with a measurement value indicative of the concentration ofanalyte present in the biological system at the time of extraction. Inone embodiment, the monitoring system uses iontophoresis to extract theanalyte from the biological system. In other embodiments, the monitoringsystem is used to extract a glucose analyte from the biological system.Further, the microprocessor can be programed to begin execution ofsampling and sensing at a defined time(s).

Additional objects, advantages and novel features of the invention willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing, or may be learned by practice of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A depicts a top plan view of an iontophoretic collection reservoirand electrode assembly for use in a transdermal sampling deviceconstructed according to the present invention.

FIG. 1B depicts the side view of the iontophoretic collection reservoirand electrode assembly shown in FIG. 1A.

FIG. 2 is a pictorial representation of an iontophoretic sampling devicewhich includes the iontophoretic collection reservoir and electrodeassembly of FIGS. 1A and 1B.

FIG. 3 is a representation of one embodiment of a bimodal electrodedesign. The figure presents an overhead and schematic view of theelectrode assembly 33. In the figure, the bimodal electrode is shown at30 and can be, for example, a Ag/AgCl iontophoretic/counter electrode.The sensing or working electrode (made from, for example, platinum) isshown at 31. The reference electrode is shown at 32 and can be, forexample, a Ag/AgCl electrode. The components are mounted on a suitablenonconductive substrate 34, for example, plastic or ceramic. Theconductive leads 37 leading to the connection pad 35 are covered by asecond nonconductive piece 36 of similar or different material. In thisexample of such an electrode the working electrode area is approximately1.35 cm². The dashed line in FIG. 3 represents the plane of thecross-sectional schematic view presented in FIG. 4.

FIG. 4 is a representation of a cross-sectional schematic view of thebimodal electrodes as they may be used in conjunction with a referenceelectrode and a hydrogel pad. In the figure, the components are asfollows: bimodal electrodes 40 and 41; sensing electrodes 42 and 43;reference electrodes 44 and 45; a substrate 46; and hydrogel pads 47 and48.

FIG. 5 is an exploded pictorial representation of components from apreferred embodiment of the automatic sampling system of the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular compositionsor biological systems as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

It must be noted that, as used in this specification and the appendedclaims, the singular forms “a”, “an” and “the” include plural referentsunless the content clearly dictates otherwise. Thus, for example,reference to “a time-dependent variable” includes a mixture of two ormore such variables, reference to “an electrochemically active species”includes two or more such species, reference to “an analyte” includesmixtures of analytes, and the like.

All publications, patents and patent applications cited herein, whethersupra or infra, are hereby incorporated by reference in their entirety.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the invention pertains. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice for testing of the present invention, the preferredmaterials and methods are described herein.

In describing and claiming the present invention, the followingterminology will be used in accordance with the definitions set outbelow.

Definitions

The terms “analyte” and “target analyte” are used herein to denote anyphysiological analyte of interest that is a specific substance orcomponent that is being detected and/or measured in a chemical,physical, enzymatic, or optical analysis. A detectable signal (e.g., achemical signal or electrochemical signal) can be obtained, eitherdirectly or indirectly, from such an analyte or derivatives thereof.Furthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

A “sampling device” or “sampling system” refers to any device forobtaining a sample from a biological system for the purpose ofdetermining the concentration of an analyte of interest. As used herein,the term “sampling” means invasive, minimally invasive or non-invasiveextraction of a substance from the biological system, generally across amembrane such as skin or mucosa. The membrane can be natural orartificial, and can be of plant or animal nature, such as natural orartificial skin, blood vessel tissue, intestinal tissue, and the like.Typically, the sampling means are in operative contact with a“reservoir,” or “collection reservoir,” wherein the sampling means isused for extracting the analyte from the biological system into thereservoir to obtain the analyte in the reservoir. A “biological system”includes both living and artificially maintained systems. Examples ofminimally invasive and noninvasive sampling techniques includeiontophoresis, sonophoresis, suction, electroporation, thermal poration,passive diffusion, microfine (miniature) lances or cannulas,subcutaneous implants or insertions, and laser devices. Sonophoresisuses ultrasound to increase the permeability of the skin (see, e.g.,Menon et al. (1994) Skin Pharmacology 7:130-139). Suitable sonophoresissampling systems are described in International Publication No. WO91/12772, published Sep. 5, 1991. Passive diffusion sampling devices aredescribed, for example, in International Publication Nos.: WO 97/38126(published Oct. 16, 1997); WO 97/42888, WO 97/42886, WO 97/42885, and WO97/42882 (all published Nov. 20, 1997); and WO 97/43962 (published Nov.27, 1997). Laser devices use a small laser beam to burn a hole throughthe upper layer of the patient's skin (see, e.g., Jacques et al. (1978)J. Invest. Dermatology 88:88-93). Examples of invasive samplingtechniques include traditional needle and syringe or vacuum sample tubedevices.

The term “collection reservoir” is used to describe any suitablecontainment means for containing a sample extracted from a biologicalsystem. For example, the collection reservoir can be a receptaclecontaining a material which is ionically conductive (e.g., water withions therein), or alternatively, it can be a material, such as, asponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the form of a disk or pad). Hydrogels are typically referredto as “collection inserts.” Other suitable collection reservoirsinclude, but are not limited to, tubes, vials, capillary collectiondevices, cannulas, and miniaturized etched, ablated or molded flowpaths.

A “housing” for the sampling system can further include suitableelectronics (e.g., microprocessor, memory, display and other circuitcomponents) and power sources for operating the sampling system in anautomatic fashion.

A “monitoring system,” as used herein, refers to a system useful forcontinually or continuously measuring a physiological analyte present ina biological system. Such a system typically includes, but is notlimited to, sampling means, sensing means, and a microprocessor means inoperative communication with the sampling means and the sensing means.

The term “artificial,” as used herein, refers to an aggregation of cellsof monolayer thickness or greater which are grown or cultured in vivo orin vitro, and which function as a tissue of an organism but are notactually derived, or excised, from a pre-existing source or host.

The term “subject” encompasses any warm-blooded animal, particularlyincluding a member of the class Mammalia such as, without limitation,humans and nonhuman primates such as chimpanzees and other apes andmonkey species; farm animals such as cattle, sheep, pigs, goats andhorses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex. Thus, adult and newbornsubjects, as well as fetuses, whether male or female, are intended to becovered.

As used herein, the term “continual measurement” intends a series of twoor more measurements obtained from a particular biological system, whichmeasurements are obtained using a single device maintained in operativecontact with the biological system over the time period in which theseries of measurements is obtained. The term thus includes continuousmeasurements.

The term “transdermal,” as used herein, includes both transdermal andtransmucosal techniques, i.e., extraction of a target analyte acrossskin or mucosal tissue. Aspects of the invention which are describedherein in the context of “transdermal,” unless otherwise specified, aremeant to apply to both transdermal and transmucosal techniques.

The term “transdermal extraction,” or “transdermally extracted” intendsany noninvasive, or at least minimally invasive sampling method, whichentails extracting and/or transporting an analyte from beneath a tissuesurface across skin or mucosal tissue. The term thus includes extractionof an analyte using iontophoresis (reverse iontophoresis),electroosmosis, sonophoresis, microdialysis, suction, and passivediffusion. These methods can, of course, be coupled with application ofskin penetration enhancers or skin permeability enhancing technique suchas tape stripping or pricking with micro-needles. The term“transdermally extracted” also encompasses extraction techniques whichemploy thermal poration, electroporation, microfine lances, microfinecanulas, subcutaneous implants or insertions, and the like.

The term “iontophoresis” intends a method for transporting substancesacross tissue by way of an application of electrical energy to thetissue. In conventional iontophoresis, a reservoir is provided at thetissue surface to serve as a container of material to be transported.Iontophoresis can be carried out using standard methods known to thoseof skill in the art, for example, by establishing an electricalpotential using a direct current (DC) between fixed anode and cathode“iontophoretic electrodes,” alternating a direct current between anodeand cathode iontophoretic electrodes, or using a more complex waveformsuch as applying a current with alternating polarity (AP) betweeniontophoretic electrodes (so that each electrode is alternately an anodeor a cathode).

The term “reverse iontophoresis” refers to the movement of a substancefrom a biological fluid across a membrane by way of an applied electricpotential or current. In reverse iontophoresis, a reservoir is providedat the tissue surface to receive the extracted material.

“Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

The term “sensing device,” “sensing means,” or “biosensor device”encompasses any device that can be used to measure the concentration ofan analyte, or derivative thereof, of interest. Preferred sensingdevices for detecting blood analytes generally include electrochemicaldevices and chemical devices. Examples of electrochemical devicesinclude the Clark electrode system (see, e.g., Updike, et al., (1967)Nature 214:986-988), and other amperometric, coulometric, orpotentiometric electrochemical devices. Examples of chemical devicesinclude conventional enzyme-based reactions as used in the Lifescan®glucose monitor (Johnson and Johnson, New Brunswick, N.J.) (see, e.g.,U.S. Pat. No. 4,935,346 to Phillips, et al.).

A “biosensor” or “biosensor device” includes, but is not limited to, a“sensor element” which includes, but is not limited to, a “biosensorelectrode” or “sensing electrode” or “working electrode” which refers tothe electrode that is monitored to determine the amount of electricalsignal at a point in time or over a given time period, which signal isthen correlated with the concentration of a chemical compound. Thesensing electrode comprises a reactive surface which converts theanalyte, or a derivative thereof, to electrical signal. The reactivesurface can be comprised of any electrically conductive material suchas, but not limited to, platinum-group metals (including, platinum,palladium, rhodium, ruthenium, osmium, and iridium), nickel, copper,silver, and carbon, as well as, oxides, dioxides, combinations or alloysthereof. Some catalytic materials, membranes, and fabricationtechnologies suitable for the construction of amperometric biosensorswere described by Newman, J. D., et al. (Analytical Chemistry 67(24),4594-4599, 1995).

The “sensor element” can include components in addition to a biosensorelectrode, for example, it can include a “reference electrode,” and a“counter electrode.” The term “reference electrode” is used herein tomean an electrode that provides a reference potential, e.g., a potentialcan be established between a reference electrode and a workingelectrode. The term “counter electrode” is used herein to mean anelectrode in an electrochemical circuit which acts as a current sourceor sink to complete the electrochemical circuit. Although it is notessential that a counter electrode be employed where a referenceelectrode is included in the circuit and the electrode is capable ofperforming the function of a counter electrode, it is preferred to haveseparate counter and reference electrodes because the referencepotential provided by the reference electrode is most stable when it isat equilibrium. If the reference electrode is required to act further asa counter electrode, the current flowing through the reference electrodemay disturb this equilibrium. Consequently, separate electrodesfunctioning as counter and reference electrodes are most preferred.

In one embodiment, the “counter electrode” of the “sensor element”comprises a “bimodal electrode.” The term “bimodal electrode” as usedherein typically refers to an electrode which is capable of functioningnon-simultaneously as, for example, both the counter electrode (of the“sensor element”) and the iontophoretic electrode (of the “samplingmeans”).

The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an electrolyte containingmaterial (e.g. gel) which contains an analyte or through which ananalyte, or a derivative thereof, flows from a source thereof; (2) iscomprised of a catalytic material (e.g., carbon, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (e.g. hydrogen peroxide) intoan electrical signal (e.g., an electrical current); and (4) defines theelectrode surface area that, when composed of a reactive material, issufficient to drive the electrochemical reaction at a rate sufficient togenerate a detectable, reproducibly measurable, electrical signal thatis correlatable with the amount of analyte present in the electrolyte.

The term “collection reservoir” and “collection insert” are used todescribe any suitable containment means for containing a sampleextracted from a biological system. The reservoir can include a materialwhich is ionically conductive (e.g., water with ions therein), whereinanother material such as a sponge-like material or hydrophilic polymeris used to keep the water in place. Such collection reservoirs can be inthe form of a hydrogel (for example, in the shape of a disk or pad).Other suitable collection reservoirs include, but are not limited to,tubes, vials, capillary collection devices, cannulas, and miniaturizedetched, ablated or molded flow paths.

An “ionically conductive material” refers to any material that providesionic conductivity, and through which electrochemically active speciescan diffuse. The ionically conductive material can be, for example, asolid, liquid, or semi-solid (e.g., in the form of a gel) material thatcontains an electrolyte, which can be composed primarily of water andions (e.g., sodium chloride), and generally comprises 50% or more waterby weight. The material can be in the form of a gel, a sponge or pad(e.g., soaked with an electrolytic solution), or any other material thatcan contain an electrolyte and allow passage therethrough ofelectrochemically active species, especially the analyte of interest.

The term “physiological effect” encompasses effects produced in thesubject that achieve the intended purpose of a therapy. In preferredembodiments, a physiological effect means that the symptoms of thesubject being treated are prevented or alleviated. For example, aphysiological effect would be one that results in the prolongation ofsurvival in a patient.

A “laminate”, as used herein, refers to structures comprised of at leasttwo bonded layers. The layers may be bonded by welding or through theuse of adhesives. Examples of welding include, but are not limited to,the following: ultrasonic welding, heat bonding, and inductively coupledlocalized heating followed by localized flow. Examples of commonadhesives include, but are not limited to, pressure sensitive adhesives,thermoset adhesives, cyanocrylate adhesives, epoxies, contact adhesives,and heat sensitive adhesives.

A “collection assembly”, as used herein, refers to structures comprisedof several layers, where the assembly includes at least one collectioninsert, for example a hydrogel. An example of a collection assembly ofthe present invention is a mask layer, collection inserts, and aretaining layer where the layers are held in appropriate, functionalrelationship to each other but are not necessarily a laminate, i.e., thelayers may not be bonded together. The layers may, for example, be heldtogether by interlocking geometry or friction.

An “autosensor assembly”, as used herein, refers to structures generallycomprising a mask layer, collection inserts, a retaining layer, anelectrode assembly, and a support tray. The autosensor assembly may alsoinclude liners. The layers of the assembly are held in appropriate,functional relationship to each other.

The mask and retaining layers are preferably composed of materials thatare substantially impermeable to the analyte (chemical signal) to bedetected (e.g., glucose); however, the material can be permeable toother substances. By “substantially impermeable” is meant that thematerial reduces or eliminates chemical signal transport (e.g., bydiffusion). The material can allow for a low level of chemical signaltransport, with the proviso that chemical signal that passes through thematerial does not cause significant edge effects at the sensingelectrode.

“Substantially planar” as used herein, includes a planar surface thatcontacts a slightly curved surface, for example, a forearm or upper armof a subject. A “substantially planar” surface is, for example, asurface having a shape to which skin can conform, i.e., contactingcontact between the skin and the surface.

By the term “printed” as used herein is meant a substantially uniformdeposition of an electrode formulation onto one surface of a substrate(i.e., the base support). It will be appreciated by those skilled in theart that a variety of techniques may be used to effect substantiallyuniform deposition of a material onto a substrate, e.g., Gravure-typeprinting, extrusion coating, screen coating, spraying, painting, or thelike.

The term “enzyme” intends any compound or material which catalyzes areaction between molecules to produce one or more reaction products. Theterm thus includes protein enzymes, or enzymatically active portions(fragments) thereof, which proteins and/or protein fragments may beisolated from a natural source, or recombinantly or syntheticallyproduced. The term also encompasses designed synthetic enzyme mimetics.

The term “time-dependent signal decline” refers to a detectable decreasein measured signal over time when no decrease or change in analyteconcentration is actually occurring. The decrease in signal over timemay be due to a number of different phenomena.

The term “signal-to-noise ratio” describes the relationship between theactual signal intended to be measured and the variation in signal in theabsence of the analyte. The terms “S/N” and “SNR” are also used to referto the signal-to-noise ratio. “Noise,” as used herein, refers to anyundesirable signal which is measured along with the intended signal.

General Methods

The present invention relates to use of a device for transdermallyextracting and measuring the concentration of a target analyte presentin a biological system. In preferred embodiments, the sensing devicecomprises a biosensor. In other preferred embodiments, a sampling deviceis used to extract small amounts of a target analyte from the biologicalsystem, and then sense and/or quantify the concentration of the targetanalyte. Measurement with the biosensor and/or sampling with thesampling device can be carried out in a continual or continuous manner.Continual or continuous measurements allow for closer monitoring oftarget analyte concentration fluctuations.

The analyte can be any specific substance or component that one isdesirous of detecting and/or measuring in a chemical, physical,enzymatic, or optical analysis. Such analytes include, but are notlimited to, amino acids, enzyme substrates or products indicating adisease state or condition, other markers of disease states orconditions, drugs of abuse, therapeutic and/or pharmacologic agents(e.g., theophylline, anti-HIV drugs, lithium, anti-epileptic drugs,cyclosporin, chemotherapeutics), electrolytes, physiological analytes ofinterest (e.g., urate/uric acid, carbonate, calcium, potassium, sodium,chloride, bicarbonate (CO₂), glucose, urea (blood urea nitrogen)lactate/lactic acid, hydroxybutyrate, cholesterol, triglycerides,creatine, creatinine, insulin, hematocrit, and hemoglobin), blood gases(carbon dioxide, oxygen, pH), lipids, heavy metals (e.g., lead, copper),and the like. In preferred embodiments, the analyte is a physiologicalanalyte of interest, for example glucose, or a chemical that has aphysiological action, for example a drug or pharmacological agent.

In order to facilitate detection of the analyte, an enzyme can bedisposed in the collection reservoir, or, if several collectionreservoirs are used, the enzyme can be disposed in several or all of thereservoirs. The selected enzyme is capable of catalyzing a reaction withthe extracted analyte (in this case glucose) to the extent that aproduct of this reaction can be sensed, e.g., can be detectedelectrochemically from the generation of a current which current isdetectable and proportional to the concentration or amount of theanalyte which is reacted. A suitable enzyme is glucose oxidase whichoxidizes glucose to gluconic acid and hydrogen peroxide. The subsequentdetection of hydrogen peroxide on an appropriate biosensor electrodegenerates two electrons per hydrogen peroxide molecule which create acurrent which can be detected and related to the amount of glucoseentering the device. Glucose oxidase (GOx) is readily availablecommercially and has well known catalytic characteristics. However,other enzymes can also be used, so long as they specifically catalyze areaction with an analyte or substance of interest to generate adetectable product in proportion to the amount of analyte so reacted.

In like manner, a number of other analyte-specific enzyme systems can beused in the invention, which enzyme systems operate on much the samegeneral techniques. For example, a biosensor electrode that detectshydrogen peroxide can be used to detect ethanol using an alcohol oxidaseenzyme system, or similarly uric acid with urate oxidase system, ureawith a urease system, cholesterol with a cholesterol oxidase system, andtheophylline with a xanthine oxidase system.

In addition, the oxidase enzyme (used for hydrogen peroxide-baseddetection) can be replaced with another redox system, for example, thedehydrogenase-enzyme NAD-NADH, which offers a separate route todetecting additional analytes. Dehydrogenase-based sensors can useworking electrodes made of gold or carbon (via mediated chemistry).Examples of analytes suitable for this type of monitoring include, butare not limited to, cholesterol, ethanol, hydroxybutyrate,phenylalanine, triglycerides, and urea. Further, the enzyme can beeliminated and detection can rely on direct electrochemical orpotentiometric detection of an analyte. Such analytes include, withoutlimitation, heavy metals (e.g., cobalt, iron, lead, nickel, zinc),oxygen, carbonate/carbon dioxide, chloride, fluoride, lithium, pH,potassium, sodium, and urea. Also, the sampling system described hereincan be used for therapeutic drug monitoring, for example, monitoringanti-epileptic drugs (e.g., phenytion), chemotherapy (e.g., adriamycin),hyperactivity (e.g., ritalin), and anti-organ-rejection (e.g.,cyclosporin).

The methods for measuring the concentration of a target analyte can begeneralized as follows. An initial step (Step A) entails obtaining a rawsignal from a sensing device, which signal is related to a targetanalyte present in the biological system. The raw signal can be obtainedusing any suitable sensing methodology including, for example, methodswhich rely on direct contact of a sensing apparatus with the biologicalsystem; methods which extract samples from the biological system byinvasive, minimally invasive, and non-invasive sampling techniques,wherein the sensing apparatus is contacted with the extracted sample;methods which rely on indirect contact of a sensing apparatus with thebiological system; and the like. In preferred embodiments of theinvention, methods are used to extract samples from the biologicalsample using minimally invasive or non-invasive sampling techniques. Thesensing apparatus used with any of the above-noted methods can employany suitable sensing element to provide the signal including, but notlimited to, physical, chemical, electrochemical, photochemical,spectrophotometric, polarimetric, colorimetric, radiometric, or likeelements. In preferred embodiments of the invention, a biosensor is usedwhich comprises an electrochemical sensing element.

After the raw signal has been obtained, the signal can undergo a datascreening method (Step B) in order to eliminate outlier signals and/orpoor (incorrect) signals using a predefined set of selection criteria.In addition, or alternatively, the raw signal can be converted in aconversion step (Step C) which can (i) remove or correct for backgroundinformation, (ii) integrate the signal over a sensing time period, (iii)perform any process which converts the signal from one signal type toanother, or (iv) perform any combination of steps (i), (ii) and/or(iii). In preferred embodiments, the conversion step entails a baselinebackground subtraction method to remove background from the raw signaland an integration step. In other embodiments, the conversion step canbe tailored for use with a sensing device that provides both active andreference (blank) signals; wherein mathematical transformations are usedto individually smooth active and reference signals, and/or to subtracta weighted reference (blank) signal from the active signal. In stillfurther embodiments, the conversion step includes correction functionswhich account for changing conditions in the biological system and/orthe biosensor system (e.g., temperature fluctuations in the biologicalsystem, temperature fluctuations in the sensor element, skinconductivity fluctuations, or combinations thereof). The result of theconversion step is an initial signal output which provides a value whichcan be correlated with the concentration of the target analyte in thebiological sample.

In a calibration step (Step D), the raw signal obtained from Step A, orthe initial signal obtained from Step B and/or Step C, is converted intoan analyte-specific value of known units to provide an interpretation ofthe signal obtained from the sensing device. The interpretation uses aone-to-one mathematical transformation to model the relationship betweena measured response in the sensing device and a correspondinganalyte-specific value. Thus, the calibration step is used herein torelate, for example, an electrochemical signal (detected by a biosensor)with the concentration of a target analyte in a biological system. Inone embodiment, the calibration step entails calibrating the sensingdevice using a single- or multi-point calibration, and then convertingpost-calibration data using correlation factors, time corrections andconstants to obtain an analyte-specific value. Further signal processingcan be used to refine the information obtained in the calibration step,for example, where a signal processing step is used to correct forsignal differences due to variable conditions unique to the sensorelement used to obtain the raw signal. In one embodiment, this furtherstep is used to correct for signal time-dependence, particularly signaldecline. In another embodiment, a constant offset term is obtained,which offset is added to the signal to account for a non-zero signal atan estimated zero analyte concentration.

The analyte value obtained using the above techniques can optionally beused in a subsequent step (Step E) to predict future (time forecasting)or past (calibration) measurements of the target analyte concentrationin the biological system. For example, a series of analyte values areobtained by performing any combination of Steps A, B, C, and/or D in aniterative manner. This measurement series is then used to predictunmeasured analyte values at different points in time, future or past.In this manner, lag times inherent in certain sampling and/or sensingtechniques can be reduced or eliminated to provide real time measurementpredictions.

In another optional step, analyte values obtained using the abovetechniques can be used in a subsequent step (Step F) to control anaspect of the biological system. In one embodiment, the analyte valueobtained in Step D is used to determine when, and at what level, aconstituent should be added to the biological system in order to controlan aspect of the biological system. In a preferred embodiment, theanalyte value can be used in a feedback control loop to control aphysiological effect in the biological system.

The above general methods (Steps A through F) are each independentlyuseful in analyte sensing systems and can, of course, be used in a widevariety of combinations selected for a particular biological system,target analyte, and/or sensing technique. For example, in certainapplications, a measurement sequence can include Steps A, C, D, E and F,in other applications, a measurement sequence can include Steps A, B, Cand D, and the like. The determination of particularly suitablecombinations is within the skill of the ordinarily skilled artisan whendirected by the instant disclosure. Furthermore, Steps C through F arepreferably embodied as one or more mathematical functions as describedherein below. These functions can thus be carried out using amicroprocessor in a monitoring system. Although these methods arebroadly applicable to measuring any chemical analyte and/or substance ina biological system, the invention is expressly exemplified for use in anon-invasive, transdermal sampling system which uses an electrochemicalbiosensor to quantify or qualify glucose or a glucose metabolite.

Step A: Obtaining the Raw Signal.

The raw signal can be obtained using any sensing device that isoperatively contacted with the biological system. Such sensing devicescan employ physical, chemical, electrochemical, spectrophotometric,polarimetric, calorimetric, radiometric, or like measurement techniques.In addition, the sensing device can be in direct or indirect contactwith the biological system, or used with a sampling device whichextracts samples from the biological system using invasive, minimallyinvasive or non-invasive sampling techniques. In preferred embodiments,a minimally invasive or non-invasive sampling device is used to obtainsamples from the biological system, and the sensing device comprises abiosensor with an electrochemical sensing element. In particularlypreferred embodiments, a sampling device is used to obtain continualtransdermal or transmucosal samples from a biological system, and theanalyte of interest is glucose.

More specifically, a non-invasive glucose monitoring device is used tomeasure changes in glucose levels in an animal subject over a wide rangeof glucose concentrations. The sampling method is based on transdermalglucose extraction and the sensing method is based on electrochemicaldetection technology. The device can be contacted with the biologicalsystem continuously, and automatically obtains glucose samples in orderto measure glucose concentration at preprogrammed intervals.

Sampling is carried out continually by non-invasively extracting glucosethrough the skin of the patient. More particularly, an iontophoreticcurrent is applied to a surface of the skin of a subject. When thecurrent is applied, ions or charged molecules pull along other unchargedmolecules or particles such as glucose which are drawn into a collectionreservoir placed on the surface of the skin. The collection reservoirmay comprise any ionically conductive material and is preferably in theform of a hydrogel which is comprised of a hydrophilic material, waterand an electrolyte.

The collection reservoir may further contain an enzyme which catalyzes areaction of glucose to form an easily detectable species. The enzyme ispreferably glucose oxidase (GOx) which catalyzes the reaction betweenglucose and oxygen and results in the production of hydrogen peroxide.The hydrogen peroxide reacts at a catalytic surface of a biosensorelectrode, resulting in the generation of electrons which create adetectable biosensor current (raw signal). Based on the amount ofbiosensor current created over a given period of time, a measurement istaken, which measurement is related to the amount of glucose drawn intothe collection reservoir over a given period of time. In a preferredembodiment, the reaction is allowed to continue until substantially allof the glucose in the collection reservoir has been subjected to areaction and is therefore no longer detectable, and the biosensorcurrent generated is related to the concentration of glucose in thesubject at the approximate time of sample collection.

When the reaction is complete, the process is repeated and a subsequentmeasurement is obtained. More specifically, the iontophoretic current isagain applied, glucose is drawn through the skin surface into thecollection reservoir, and the reaction is catalyzed in order to create abiosensor current. These sampling (extraction) and sensing operationsare integrated such that glucose is extracted into the hydrogelcollection pad where it contacts the GOx enzyme. The GOx enzyme convertsglucose and oxygen in the hydrogel to hydrogen peroxide which diffusesto the sensor and is catalyzed by the sensor to regenerate oxygen andform electrons. The electrons generate an electrical signal that can bemeasured, analyzed, and correlated to blood glucose.

Optionally, one or more additional “active” collection reservoirs (eachcontaining the GOx enzyme) can be used to obtain measurements. In oneembodiment, two active collection reservoirs are used, and an average istaken between signals from the reservoirs for each measurement timepoint. Obtaining multiple signals, and then averaging reads from eachsignals, allows for signal smoothing of unusual data points from asensor that otherwise may not have been detected by data screeningtechniques. Furthermore, skin site variability can be detected, and“lag” and/or “lead” differences in blood glucose changes relative toextracted glucose changes can be mitigated. In another embodiment, asecond collection reservoir can be provided which does not contain theGOx enzyme. This second reservoir can serve as an internal reference(blank) for the sensing device, where a biosensor is used to measure the“blank” signal from the reference reservoir which signal is then used ina blank subtraction step as described below.

A generalized method for continual monitoring of a physiological analyteis disclosed in International Publication No. WO 97/24059, publishedJul. 10, 1997, which publication is incorporated herein by reference. Asnoted in that publication, the analyte is extracted into a reservoircontaining a hydrogel which is preferably comprised of a hydrophilicmaterial of the type described in International Publication No. WO97/02811, published Jan. 30, 1997, which publication is incorporatedherein by reference. Suitable hydrogel materials include polyethyleneoxide, polyacrylic acid, polyvinylalcohol and related hydrophilicpolymeric materials combined with water to form an aqueous gel.

In the above non-invasive glucose monitoring device, a biosensorelectrode is positioned on a surface of the hydrogel opposite thesurface contacting the skin. The sensor electrode acts as a detectorwhich detects current generated by hydrogen peroxide in the redoxreaction, or more specifically detects current which is generated by theelectrons generated by the redox reaction catalyzed by the platinumsurface of the electrode. The details of such electrode assemblies anddevices for iontophoretic extraction of glucose are disclosed inInternational Publication No. WO 96/00110, published Jan. 4, 1996, andInternational Publication No. WO 97/10499, published Mar. 2, 1997, whichpublications are also incorporated herein by reference.

Referring now to FIGS. 1A and 1B, an iontophoretic collection reservoirand electrode assembly for use in a transdermal sensing device isgenerally indicated at 2. The assembly comprises two iontophoreticcollection reservoirs, 4 and 6, each having a conductive medium 8, and10 (preferably cylindrical hydrogel pads), respectively disposedtherein. First (12) and second (14) ring-shaped iontophoretic electrodesare respectively contacted with conductive medium 8 and 10. The firstiontophoretic electrode 12 surrounds three biosensor electrodes whichare also contacted with the conductive medium 8, a working electrode 16,a reference electrode 18, and a counter electrode 20. A guard ring 22separates the biosensor electrodes from the iontophoretic electrode 12to minimize noise from the iontophoretic circuit. Conductive contactsprovide communication between the electrodes and an associated powersource and control means as described in detail below. A similarbiosensor electrode arrangement can be contacted with the conductivemedium 10, or the medium can not have a sensor means contactedtherewith.

Referring now to FIG. 2, an exploded view of the key components from apreferred embodiment of an iontophoretic sampling system is presented.In FIG. 2, the iontophoretic collection reservoir and electrode assembly2 of FIGS. 1A and 1B is shown in exploded view in combination with asuitable iontophoretic sampling device housing 32. The housing can be aplastic case or other suitable structure which preferably is configuredto be worn on a subjects arm in a manner similar to a wrist watch. Ascan be seen, conductive media 8 and 10 (hydrogel pads) are separablefrom the assembly 2; however, when the assembly 2 and the housing 32 areassembled to provide an operational iontophoretic sampling device 30,the media are in contact with the electrodes to provide a electricalcontact therewith.

In one embodiment, the electrode assemblies can include bimodalelectrodes as shown in FIG. 3.

Referring now to FIG. 5, an exploded view of the key components from oneembodiment of an iontophoretic sampling system (e.g., one embodiment ofan autosensor assembly) is presented. The sampling system componentsinclude two biosensor/iontophoretic electrode assemblies, 504 and 506,each of which have an annular iontophoretic electrode, respectivelyindicated at 508 and 510, which encircles a biosensor 512 and 514. Theelectrode assemblies 504 and 506 are printed onto a polymeric substrate516 which is maintained within a sensor tray 518. A collection reservoirassembly 520 is arranged over the electrode assemblies, wherein thecollection reservoir assembly comprises two hydrogel inserts 522 and 524retained by a gel retaining layer 526 and a mask layer 528.

In one embodiment, the electrode assemblies can include bimodalelectrodes as shown in FIG. 3. Modifications and additions to theembodiment of FIG. 5 will be apparent to those skilled in the art inlight of the teachings of the present specification.

The components described herein are intended for use in a automaticsampling device which is configured to be worn like an ordinarywristwatch. As described in International Publication No. WO 96/00110,published Jan. 4, 1996, the wristwatch housing (not shown) containsconductive leads which communicate with the iontophoretic electrodes andthe biosensor electrodes to control cycling and provide power to theiontophoretic electrodes, and to detect electrochemical signals producedat the biosensor electrode surfaces. The wristwatch housing can furtherinclude suitable electronics (e.g., microprocessor, memory, display andother circuit components) and power sources for operating the automaticsampling system.

Modifications and additions to the embodiment of FIG. 2 will be apparentto those skilled in the art in light of the teachings of the presentspecification.

A power source (e.g., one or more rechargeable or nonrechargeablebatteries) can be disposed within the housing 32 or within the straps 34which hold the device in contact with a skin or mucosal surface of asubject. In use, an electric potential (either direct current or a morecomplex waveform) is applied between the two iontophoretic electrodes 12and 14 such that current flows from the first iontophoretic electrode12, through the first conductive medium 8 into the skin or mucosalsurface, and then back out through the second conductive medium 10 tothe second iontophoretic electrode 14. The current flow is sufficient toextract substances including an analyte of interest through the skininto one or both of collection reservoirs 4 and 6. The electricpotential may be applied using any suitable technique, for example, theapplied current density may be in the range of about 0.01 to 0.5 mA/cm².In a preferred embodiment, the device is used for continual orcontinuous monitoring, and the polarity of iontophoretic electrodes 12and 14 is alternated at a rate of about one switch every 10 seconds toabout one switch every hour so that each electrode is alternately acathode or an anode. The housing 32 can further include an optionaltemperature sensing element (e.g., a thermistor, thermometer, orthermocouple device) which monitors the temperature at the collectionreservoirs to enable temperature correction of sensor signals asdescribed in detail below. The housing can also include an optionalconductance sensing element (e.g., an integrated pair of electrodes)which monitors conductance at the skin or mucosal surface to enable datascreening correction or invalidation of sensor signals as also describedin detail below.

After a suitable iontophoretic extraction period, one or both of thesensor electrode sets can be activated in order to detect extractedsubstances including the analyte of interest. Operation of theiontophoretic sampling device 30 is controlled by a controller 36 (e.g.,a microprocessor), which interfaces with the iontophoretic electrodes,the sensor electrodes, the power supply, the optional temperature and/orconductance sensing elements, a display and other electronics. Forexample, the, controller 36 can include a programmable a controlledcircuit source/sink drive for driving the iontophoretic electrodes.Power and reference voltage are provided to the sensor electrodes, andsignal amplifiers can be used to process the signal from the workingelectrode or electrodes. In general, the controller discontinues theiontophoretic current drive during sensing periods. A sensor confidenceloop can be provided for continually monitoring the sampling system toinsure proper operations.

In a further aspect, the sampling device can operate in an alternatingpolarity mode using first and second bimodal electrodes (FIG. 4, 40 and41) and two collection reservoirs (FIG. 4, 47 and 48). Each bi-modalelectrode (FIG. 3, 30; FIG. 4, 40 and 41) serves two functions dependingon the phase of the operation: (1) an electro-osmotic electrode (oriontophoretic electrode) used to electrically draw analyte from a sourceinto a collection reservoir comprising water and an electrolyte, and tothe area of the electrode subassembly; and (2) as a counter electrode tothe first sensing electrode at which the chemical compound iscatalytically converted at the face of the sensing electrode to producean electrical signal.

The reference (FIG. 4, 44 and 45; FIG. 3, 32) and sensing electrodes(FIG. 4, 42 and 43; FIG. 3, 31), as well as, the bimodal electrode (FIG.4, 40 and 41; FIG. 3, 30) are connected to a standard potentiostatcircuit during sensing. In general, practical limitations of the systemrequire that the bimodal electrode will not act as both a counter andiontophoretic electrode simultaneously.

The general operation of an iontophoretic sampling system is thecyclical repetition of two phases: (1) a reverse-iontophoretic phase,followed by a (2) sensing phase. During the reverse iontophoretic phase,the first bimodal electrode (FIG. 4, 40) acts as an iontophoreticcathode and the second bimodal electrode (FIG. 4, 41) acts as aniontophoretic anode to complete the circuit. Analyte is collected in thereservoirs, for example, a hydrogel (FIG. 4, 47 and 48). At the end ofthe reverse iontophoretic phase, the iontophoretic current is turnedoff. During the sensing phase, in the case of glucose, a potential isapplied between the reference electrode (FIG. 4, 44) and the sensingelectrode (FIG. 4, 42). The chemical signal reacts catalytically on thecatalytic face of the first sensing electrode (FIG. 4, 42) producing anelectrical current, while the first bi-modal electrode (FIG. 4, 40) actsas a counter electrode to complete the electrical circuit.

The electrode described is particularly adapted for use in conjunctionwith a hydrogel collection reservoir system for monitoring glucoselevels in a subject through the reaction of collected glucose with theenzyme glucose oxidase present in the hydrogel matrix.

The bi-modal electrode is preferably comprised of Ag/AgCl. Theelectrochemical reaction which occurs at the surface of this electrodeserves as a facile source or sink for electrical current. This propertyis especially important for the iontophoresis function of the electrode.Lacking this reaction, the iontophoresis current could cause thehydrolysis of water to occur at the iontophoresis electrodes causing pHchanges and possible gas bubble formation. The pH changes to acidic orbasic pH could cause skin irritation or burns. The ability of an Ag/AgClelectrode to easily act as a source of sink current is also an advantagefor its counter electrode function. For a three electrodeelectrochemical cell to function properly, the current generationcapacity of the counter electrode should not limit the speed of thereaction at the sensing electrode. In the case of a large sensingelectrode, the counter electrode should be able to sourceproportionately larger currents.

The design of the sampling system provides for a larger sensingelectrode (see for example, FIG. 3) than previously designed.Consequently, the size of the bimodal electrode should be sufficient sothat when acting as a counter electrode with respect to the sensingelectrode the counter electrode does not become limiting the rate ofcatalytic reaction at the sensing electrode catalytic surface.

Two methods exist to ensure that the counter electrode does not limitthe current at the sensing electrode: (1) the bi-modal electrode is mademuch larger than the sensing electrode, or (2) a facile counter reactionis provided.

During the reverse iontophoretic phase, the power source provides acurrent flow to the first bi-modal electrode to facilitate theextraction of the chemical signal into the reservoir. During the sensingphase, the power source is used to provide voltage to the first sensingelectrode to drive the conversion of chemical signal retained inreservoir to electrical signal at the catalytic face of the sensingelectrode. The power source also maintains a fixed potential at theelectrode where, for example hydrogen peroxide is converted to molecularoxygen, hydrogen ions, and electrons, which is compared with thepotential of the reference electrode during the sensing phase. While onesensing electrode is operating in the sensing mode it is electricallyconnected to the adjacent bimodal electrode which acts as a counterelectrode at which electrons generated at the sensing electrode areconsumed.

The electrode sub-assembly can be operated by electrically connectingthe bimodal electrodes such that each electrode is capable offunctioning as both an iontophoretic electrode and counter electrodealong with appropriate sensing electrode(s) and reference electrode(s),to create standard potentiostat circuitry.

A potentiostat is an electrical circuit used in electrochemicalmeasurements in three electrode electrochemical cells. A potential isapplied between the reference electrode and the sensing electrode. Thecurrent generated at the sensing electrode flows through circuitry tothe counter electrode (i.e., no current flows through the referenceelectrode to alter its equilibrium potential). Two independentpotentiostat circuits can be used to operate the two biosensors. For thepurpose of the present sampling system, the electrical current measuredat the sensing electrode subassembly is the current that is correlatedwith an amount of chemical signal.

With regard to continual operation for extended periods of time, Ag/AgClelectrodes are provided herein which are capable of repeatedly forming areversible couple which operates without unwanted electrochemical sidereactions (which could give rise to changes in pH, and liberation ofhydrogen and oxygen due to water hydrolysis). The Ag/AgCl electrodes ofthe present sampling system are thus formulated to withstand repeatedcycles of current passage in the range of about 0.01 to 1.0 mA per cm²of electrode area. With regard to high electrochemical purity, theAg/AgCl components are dispersed within a suitable polymer binder toprovide an electrode composition which is not susceptible to attack(e.g., plasticization) by components in the collection reservoir, e.g.,the hydrogel composition. The electrode compositions are also formulatedusing analytical- or electronic-grade reagents and solvents, and thepolymer binder composition is selected to be free of electrochemicallyactive contaminants which could diffuse to the biosensor to produce abackground current.

Since the Ag/AgCl iontophoretic electrodes must be capable of continualcycling over extended periods of time, the absolute amounts of Ag andAgCl available in the electrodes, and the overall Ag/AgCl availabilityratio, can be adjusted to provide for the passage of high amounts ofcharge. Although not limiting in the sampling system described herein,the Ag/AgCl ratio can approach unity. In order to operate within thepreferred system which uses a biosensor having a geometric area of 0.1to 3 cm², the iontophoretic electrodes are configured to provide anapproximate electrode area of 0.3 to 1.0 cm², preferably about 0.85 cm².These electrodes provide for reproducible, repeated cycles of chargepassage at current densities ranging from about 0.01 to 1.0 mA/cm² ofelectrode area. More particularly, electrodes constructed according tothe above formulation parameters, and having an approximate electrodearea of 0.85 cm², are capable of a reproducible total charge passage (inboth anodic and cathodic directions) of 270 mC, at a current of about0.3 mA (current density of 0.35 mA/cm²) for 48 cycles in a 24 hourperiod.

Once formulated, the Ag/AgCl electrode composition is affixed to asuitable rigid or flexible nonconductive surface as described above withrespect to the biosensor electrode composition. A silver (Ag) underlayeris first applied to the surface in order to provide uniform conduction.The Ag/AgCl electrode composition is then applied over the Ag underlayerin any suitable pattern or geometry using various thin film techniques,such as sputtering, evaporation, vapor phase deposition, or the like, orusing various thick film techniques, such as film laminating,electroplating, or the like. Alternatively, the Ag/AgCl composition canbe applied using screen printing, pad printing, inkjet methods, transferroll printing, or similar techniques. Preferably, both the Ag underlayerand the Ag/AgCl electrode are applied using a low temperature screenprint onto a polymeric substrate. This low temperature screen print canbe carried out at about 125 to 160° C., and the screening can be carriedout using a suitable mesh, ranging from about 100-400 mesh.

User control can be carried out using push buttons located on thehousing 32, and an optional liquid crystal display (LCD) can providevisual prompts, readouts and visual alarm indications. Themicroprocessor generally uses a series of program sequences to controlthe operations of the sampling device, which program sequences can bestored in the microprocessor's read only memory (ROM). Embedded software(firmware) controls activation of measurement and display operations,calibration of analyte readings, setting and display of high and lowanalyte value alarms, display and setting of time and date functions,alarm time, and display of stored readings. Sensor signals obtained fromthe sensor electrodes are processed before storage and display by one ormore signal processing functions or algorithms which are described indetail below. The microprocessor can also include an electronicallyerasable, programmable, read only memory (EEPROM) for storingcalibration parameters (as described in detail below), user settings andall downloadable sequences.

Step B: Data Screening Methodologies.

The raw signal obtained from the above-described glucose monitoringdevice can be screened to detect deviations from expected behavior whichare indicative of poor or incorrect signals that will not correlate withblood glucose. Signals that are identified as poor or incorrect in thisdata screen may be discarded or otherwise corrected for prior to anysignal processing and/or conversion in order to maintain data integrity.In the method of the invention, an objective set of selection criteriais established which can then be used to accept or discard signals fromthe sensing device. These selection criteria are device- andanalyte-specific, and can be arrived at empirically by way of testingvarious devices in particular applications.

In the particular-context of transdermal blood glucose monitoring usingiontophoretic extraction and electrochemical detection, the followingdata screens can be employed. As discussed above, the iontophoreticextraction device can include two collection reservoirs. Thus, inactive/blank systems, wherein one reservoir is active (contains the GOxenzyme) and one reservoir is blank, each reservoir contains aniontophoretic electrode and a sensing electrode. Signals from both theactive and the blank reservoirs are screened, and an error in either theactive, or the active and blank signal can be used to invalidate orcorrect the measurement from the cycle. In multiple active systems(wherein two or more reservoirs contain the GOx enzyme and iontophoreticand sensing electrodes), signals from one or more of the activereservoirs are screened, and an error can be used to invalidate orcorrect the measurement from the cycle.

As with any chemical sensing method, transient changes in temperatureduring or between measurement cycles, or between measurements of blankand active signals, can alter background signal, reaction constantsand/or diffusion coefficients. Accordingly, a temperature sensor is usedto monitor changes in temperature over time. A maximum temperaturechange over time (d(temp)/d(time)) threshold value can then be used in adata screen to invalidate a measurement. Such a threshold value can, ofcourse, be set at any objective level, which in turn can be empiricallydetermined depending upon the particular extraction/sensing device used,how the temperature measurement is obtained, and the analyte beingdetected. Absolute temperature threshold criteria can also be employed,wherein detection of high and/or low temperature extremes can be used ina data screen to invalidate a measurement. Temperature monitoring can becarried out using a separate, associated temperature sensing device, or,preferably using a temperature sensor that is integral with the sensingdevice. A large number of temperature sensing elements are known in theart (e.g., thermometers, thermistors, thermocouples, and the like) whichcan be used to monitor the temperature in the collection reservoirs.

Another data screen entails monitoring physiological conditions in thebiological system, particularly monitoring for a perspiration threshold.In this regard, perspiration contains glucose, and perspirationoccurring rapidly and in sufficient quantities may affect the detectedsignal either before or during biosensor measurement. Accordingly, asensor can be used to monitor perspiration levels for a givenmeasurement cycle at time points before, during, and/or afteriontophoresis, and before, during, and/or after glucose sensing.Detection of perspiration levels that exceed an objective threshold isthen used in a data screen to invalidate poor measurements. Although anumber of different mechanisms can be used, skin conductance can bereadily measured with a device contacted with the skin. Skinconductivity is related to perspiration. In one embodiment, if skinconductance as measured by a conductivity detector is greater than apredetermined level, then the corresponding measurement is invalidated.

Yet further data screens which are used in the practice of the inventiontake into consideration the expected behavior of the sampling/sensingdevice. In iontophoretic sampling, for example, there is a skinequilibration period before which measurements will generally be lessaccurate. During this equilibration period, the system voltage can beassessed and compared against an objective high voltage threshold. Ifthis high voltage limit is exceeded, a data screen is used to excludethe corresponding analyte measurement, since the iontophoretic currentwas not at a target value due to high skin resistance (as indicted bythe high voltage level).

In addition, the electrochemical signal during each sensing cycle isexpected to behave as a smooth, monotonically decreasing signal whichrepresents depletion of the hydrogen peroxide by the sensor electrode.Significant departure from this expected behavior is indicative of apoor or incorrect measurement (e.g., a non-monotonically decreasingsignal is indicative of excessive noise in the biosensor signal), andthus monitoring signal behavior during sensing operations provides yet afurther data screen for invalidating or correcting measurements.

Raw signal thresholds can also be used in the data screening method ofthe present invention. For example, any sensor reading that is less thansome minimum threshold can indicate that the sampling/sensing device isnot operating correctly, for example, where the biosensor electrode isdisconnected. In addition, any chemical sensor will have a maximum rangein which the device can operate reliably. A reading greater than somemaximal value, then, indicates that the measurement is off-scale, andthus possibly invalid. Accordingly, minimum and maximum signalthresholds are used herein as data screens to invalidate or correctmeasurements. Such minimum and maximum thresholds can likewise beapplied to background measurements.

A general class of screens can be applied that detect changes in signal,background, or voltage measurements. These screens are useful to assessthe consistency of measurements and can detect problems orinconsistencies in the measurements. Error messages can be relayed to adisplay screen on the monitoring device, and/or, recorded to a log.Examples of such screens include the following:

(i) signal—Peak Stability. A large change in the peak of a sensorreading indicates a noisy signal. The peak of any given cathodal halfcycle is defined as the difference between the first biosensor point andthe temperature corrected average of the last two points from theprevious anodal half cycle. If the percentage difference betweensuccessive peaks from the same sensor is greater than a predeterminedvalue, for example, 30%, then an error is indicated.

(ii) background—Background Precision. Divergent readings at the end ofbiosensing indicate an unstable biosensor signal. Because these readingsare used to assess background current for a particular cycle, anunstable signal may lead to an erroneous data point. If the differencebetween the last two anodal points (where the last two anodal points aretypically the last two biosensor currents measured after anodalextraction) used to calculate the baseline is greater than or equal to apredetermined value, for example, 6 nA (or, e.g., a percentage of thefirst anodal point relative to the second anodal point), then an erroris indicated.

(iii) background—Background Stability. This check is to determine if thebackground current is changing too excessively, which indicates a noisysignal and can result in inaccurate glucose readings. If the percentagedifference between successive background measurements is greater than orequal to a predetermined value, for example, 15%, then an error isindicated.

(iv) voltage—Voltage Stability. If the glucose monitoring device ismechanically disturbed, there can be a larger change (e.g., largerrelative to when the monitor is functioning under normal conditions) iniontophoresis voltage. This could lead to an aberrant reading. If thepercentage difference between successive cathodal or anodaliontophoresis voltages is grater than a predetermined value, forexample, 15%, then an error is indicated.

(v) voltage—Reference Electrode Check. When the electrode assemblyincludes a reference electrode (as when, for example, a bimodalelectrode is employed) this check establishes the connectivity of thereference electrode to the sampling device and to the working electrode.The biosensor is activated such that a current should flow from theworking electrode to the reference electrode. If the current measured isless than a threshold value, then an error is indicated and themeasurement sequence can be terminated.

As will be appreciated by one of ordinary skill in the art upon readingthis specification, a large number of other data screens can be employedwithout departing from the spirit of the present invention.

Step C: The Conversion Step.

Continuing with the method of the invention, the above-describediontophoretic sampling device is used to extract the analyte from thebiological system, and a raw amperometric signal (e.g., nanoampere (nA)signal) is generated from the associated electrochemical biosensordevice. This raw signal can optionally be subjected to a data screeningstep (Step B) to eliminate poor or incorrect signals, or can be entereddirectly into a conversion step to obtain an initial signal output whichis indicative of the amount of analyte extracted by the sampling system.

I. Ways of Obtaining Integrated Signals

1. Baseline Background.

In one embodiment, the raw or screened raw signal is processed in theconversion step in order to remove or correct for background informationpresent in the signal. For example, many sensor devices will have asignal whether or not an analyte of interest is present, i.e., thebackground signal. One such background signal is the “baselinebackground,” which, in the context of electrochemical detection, is acurrent (nA) generated by the sensing device independent of the presenceor absence of the analyte of interest. This baseline backgroundinterferes with measurement of analyte of interest, and the amount ofbaseline background can vary with time, temperature and other variablefactors. In addition, electrochemically active interfering speciesand/or residual analyte can be present in the device which will furtherinterfere with measurement of the analyte of interest.

This background can be transient background, which is a currentgenerated independent of the presence or absence of the analyte ofinterest and which decreases over the time of sensor activation on thetime scale of a measurement, eventually converging with the baselinebackground signal.

Accordingly, in one embodiment of the invention, a baseline backgroundsubtraction method is used during the conversion step in order to reduceor eliminate such background interferences from the measured initialsignal output. The subtraction method entails activation of theelectrochemical sensor for a sufficient period of time to substantiallyreduce or eliminate residual analyte and/or electrochemical signal thatis not due to the analyte (glucose). After the device has been activatedfor a suitable period of time, and a stable signal is obtained, ameasurement is taken from the sensor which measurement can then be usedto establish a baseline background signal value. This background signalvalue is subtracted from an actual signal measurement value (whichincludes both analyte-specific and background components) to obtain acorrected measurement value. This baseline background subtraction methodcan be expressed using the following function:i(τ)=i _(raw)(τ)−i _(bkgnd)(τ)wherein: (i_(raw)(τ)) is the current measured by the sensor (in nA) attime τ; (τ) is the time after activation of the sensor; (i_(bkgnd)(τ))is the background current (in nA); and (i(τ)) is the corrected current(in nA). Measurement of the baseline background signal value is takenclose in time to the actual signal measurement in order to account fortemperature fluctuations, background signal drift, and like variables inthe baseline background subtraction procedure. The baseline backgroundsignal value can be integrated for use with coulometric signalprocessing, or used as a discrete signal value in amperometric signalprocessing. In particular embodiments of the invention, continualmeasurement by the iontophoretic sampling device provides a convenientsource for the baseline background measurement, that is, after aninitial measurement cycle has be completed, the baseline backgroundmeasurement can be taken from a previous measurement (sensing) cycle.

2. Temperature Correcting Baseline Background.

In yet another embodiment of the invention, the conversion step is usedto correct for changing conditions in the biological system and/or thebiosensor system (e.g., temperature fluctuations in the biologicalsystem, temperature fluctuations in the biosensor element, orcombinations thereof). Temperature can affect the signal in a number ofways, such as by changing background, reaction constants, and/ordiffusion coefficients. Accordingly, a number of optional temperaturecorrection functions can be used in order to reduce thesetemperature-related effects on the signal.

In order to correct for the effect that temperature fluctuations ordifferences may have on the baseline background subtracted signal, thefollowing temperature correction step can be carried out. Moreparticularly, to compensate for temperature fluctuations, temperaturemeasurements can be taken at each measurement time point within themeasurement cycle, and this information can be used to base atemperature correction algorithm which adjusts the background current atevery time point depending on the difference in temperature between thattime point and the temperature when the previous background current wasmeasured. This particular temperature correction algorithm is based onan Arrhenius relationship between the background current andtemperature.

The temperature correction algorithm assumes an Arrhenius-typetemperature dependence on the background current, such as:

$i_{bkgnd} = {A\;{\exp\left\lbrack \frac{- {K1}}{T} \right\rbrack}}$wherein: (i_(bkgnd)) is the background current; (A) is a constant; (K1)is termed the “Arrhenius slope” and is an indication of how sensitivethe current is to changes in temperature; and (T) is the temperature in° K.

Plotting the natural log of the background current versus the reciprocalof temperature provides a linear function having a slope of (−K1). Usinga known or derived value of K1 allows the baseline current at any time(τ) to be corrected using the following function (which is referred toherein as the “K1 temperature correction”):

$i_{{bkgnd},{corrected}} = {i_{{bkgnd},\tau_{0}}{\exp\left\lbrack {- {{K1}\left( {\frac{1}{T_{\tau}} - \frac{1}{T_{\tau_{0}}}} \right)}} \right\rbrack}}$wherein: (I_(bkgnd,corrected)) is the temperature corrected baselinecurrent; (i_(bkgnd,τ0)) is the baseline current at some referencetemperature T_(τ0), for example, the baseline background measurementtemperature; (K1) is the temperature correction constant; and (T_(τ)) isthe temperature at time τ. For the purposes of the invention,(i_(bkgnd,τ0)) is usually defined as the “previous” baseline current. Ascan be seen, instead of making a time-independent estimation of thebaseline current, the K1 temperature correction adjusts the baselinecurrent in an Arrhenius fashion depending upon whether the temperatureincreases or decreases during or between biosensor cycles. Determinationof the constant K1 can be obtained by plotting the natural log of thebackground current versus the reciprocal of the temperature for alearning set of data, and then using a best fit analysis to fit thisplot with a line having a slope (−K1).

Raw or screened amperometric signals from Step A or Step B, respectively(whether or not subjected to the above-described baseline backgroundsubtraction and/or K1 temperature correction), can optionally be refinedin the conversion step to provide integrated coulometric signals. In oneparticular embodiment of the invention, any of the above amperometricsignals (e.g., the current generated by the sensor) can be converted toa coulometric signal (nanocoulombs (nC)), which represents theintegration of the current generated by the sensor over time to obtainthe charge that was produced by the electrochemical reaction.

In one embodiment, integration is carried out by operating the biosensorin a coulometric (charge-measuring) mode. Measuring the total amount ofcharge that passes through the biosensor electrode during a measurementperiod is equivalent to mathematically integrating the current overtime. By operating in the coulometric mode, changes in diffusionconstants resulting from temperature fluctuations, possible changes inthe diffusion path length caused by uneven or non-uniform reservoirthickness, and changes in sensor sensitivity, have little effect on theintegrated signal, whereas these parameters may have a greater effect onsingle point (current) measurements. Alternatively, a functionallyequivalent coulometric measurement can be mathematically obtained in themethod of the invention by taking discrete current measurements atselected, preferably small, time intervals, and then using any of anumber of algorithms to approximate the integral of the time-currentcurve. For example, integrated signal can be obtained as follows:

Y = ∫_(τ₁)^(τ₂)i(τ) 𝕕τwherein: (Y) is the integrated signal (in nC); and ((i(τ)) is a currentat time τ, and can be equal to i_(raw)(τ) for an uncorrected raw signal,or i_(raw)(τ)−i_(bkgnd)(τ) for a baseline background subtracted signal,or i_(raw)(τ)−i_(bkgnd,corrected)(τ) for a baseline backgroundsubtracted and temperature corrected signal.

3. Temperature Correction of Active Versus Blank Integrals.

An additional temperature correction algorithm can be used herein tocompensate for temperature dependence of a transient background (blank)signal. That is, in the active/blank sampling system exemplifiedhereinabove, the analyte measurement (blood glucose) is generated byintegrating an active signal and subtracting therefrom a blank signal(see the blank subtraction method, infra). The blank integral may be“artifactually” high or low depending upon whether blank signal wasmeasured at a higher or lower temperature than the active signal. Inorder to normalize the blank integral to the temperature at which theactive signal was measured, the following function can be used (which isreferred to herein as the “K2 temperature correction”):

$Y_{{blank},{corrected}} = {Y_{blank}{\exp\left\lbrack {- {{K2}\left( {\frac{1}{\overset{\_}{T_{act}^{n}}} - \frac{1}{\overset{\overset{\_}{\;}}{T_{blank}^{n}}}} \right)}} \right\rbrack}}$wherein: (Y_(blank,corrected)) is the corrected blank integral;(Y_(blank)) is the uncorrected blank integral (in nC); (K2) is the“blank integral correction constant”; and (T^(n) _(act)) and (T^(n)_(blank)) are the average temperature of the active and blank signal,respectively. The average temperature is obtained from averaging thefirst n temperatures, such that (n) is also an adjustable parameter.Determination of the constant K2 can be obtained from an Arrhenius plotof the log of the blank integral against 1/T^(n) _(blank), using thereciprocal of the average of the first n temperature values, and thenusing a best fit analysis to fit this plot with a line having a slope(−K2).

Alternative temperature corrections which can be performed during theconversion step are as follows. In one embodiment, an integral averagetemperature correction is used wherein, for each measurement cycle, theintegral average temperature is determined by the function:

$< T>={\frac{1}{T_{f}}{\int_{0}^{T_{f}}{T\ {\mathbb{d}t}}}}$and then correcting for the temperature at subsequent time points usingthe function:

$Y_{t,{corrected}} = {Y_{t}{\exp\left\lbrack {- {a\left( \frac{< T_{t} > {- {< T_{ref} >}}}{< T_{ref} >} \right)}} \right\rbrack}}$wherein: (Y_(t)) is the uncorrected signal at time t; (Y_(t,corrected))is the corrected signal at time t; (<T_(t)>) is the integral averagetemperature at time t; (<T_(ref)>) is the integral average temperatureat the reference time (e.g., the calibration time); (t) is the timeafter sensor measurement is first initiated; and (a) is an adjustableparameter which is fit to the data.

In other embodiments, temperature correction functions can be used tocorrect for temperature differences between multiple active signals, orbetween active and blank signals. For example, in the active/blanksensing device exemplified herein, blank subtraction is used to cancelout much of the temperature-dependence in the active signal. However,temperature transients during the monitoring period will result invarying background currents, which can result in signal errors when thecurrent is multiplied by the total integration time in the instantconversion step. This is particularly true where the active and blankintegrals are disjointed in time, and thus possibly comprised of sets ofbackground current values that occurred at different temperatures. 4.Anodal Subtraction.

In yet another alternative temperature correction, temperaturemeasurements taken in the active and blank reservoirs at alternatinganodal and cathodal phases during a measurement cycle are used in asubtraction method in order to reduce the impact of temperaturefluctuations on the signals. In this regard, the active/blank reservoiriontophoretic sampling system can be run under conditions whichalternate the active and blank reservoirs between anodal and cathodalphases during a measurement cycle. This allows the blank anodal signalto be measured at the same time as the active cathode signal, andtemperature variations will likely have similar impact on the twosignals. The temperature correction function thus subtracts an adjustedanodal signal (taken at the same time as the cathodal signal) from thecathodal signal in order to account for the effect of temperature on thebackground. More particularly, a number of related temperaturecorrection functions which involve fractional subtraction of blank anodesignals can be summarized as follows:

$\begin{matrix}{Y = {Y_{{act},{cath}} - {d*Y_{{blank},{an}}}}} \\{Y = {Y_{{act},{cath}} - {d*\left\lbrack {Y_{{blank},{an}} - \left( {Y_{{act},{an}} - Y_{{blank},{cath}}} \right)} \right\rbrack}}} \\{Y = \left. {Y_{{act},{cath}} - {d*\left\lbrack {Y_{{blank},{an}} - \left( {Y_{{act},{an}} - Y_{{blank},{cath}}} \right)} \right\rbrack}} \right|_{{{ave}\mspace{14mu} t_{1}},t_{2}}} \\{Y = \left. {Y_{{act},{cath}} - {d*\left\lbrack {Y_{{blank},{an}} - \left( {Y_{{blank},{an}} - Y_{{blank},{cath}}} \right)} \right\rbrack}} \right|_{{ave}\mspace{14mu}{t_{1}--}t_{2}}} \\{Y = \left. {Y_{{act},{cath}} - {d*\left( {Y_{{blank},{an}} - {AOS}} \right)*\left\lbrack \frac{Y_{{blank},{cath}}}{Y_{{act},{an}} - {AOS}} \right\rbrack}} \right|_{{{ave}\mspace{14mu} t_{1}},t_{2}}} \\{Y = \left. {Y_{{act},{cath}} - {d*\left( {Y_{{blank},{an}} - {AOS}} \right)*\left\lbrack \frac{Y_{{blank},{cath}}}{Y_{{act},{an}} - {AOS}} \right\rbrack}} \right|_{{ave}\mspace{14mu}{t_{1}--}t_{2}}}\end{matrix}$wherein: (Y_(act, cath)) is the active signal in the cathodal phase (innC); (Y_(blank, an)) is the blank signal in the anodal phase (in nC);(Y_(act, an)) is the active signal in the anodal phase (in nC)(Y_(blank, cath)) is the blank signal in the cathodal phase (in nC); (Y)is the “blank anode subtracted” signal; (ave t₁, t₂) is the average ofsignals taken at two time points t₁ and t₂; (ave t₁--t₂) is the averageof signals taken over the time period of t₁-t₂; (d) is a universalfractional weight and is generally a function of time; and (AOS) is auniversal anodal offset which can be empirically obtained using standardmathematical techniques, and optionally adjusted using data taken fromtwo previous time points, t₁ and t₂ (i.e., ave t₁,t₂) or using theaverage of data taken over the time period of t₁-t₂ (i.e., ave t₁--t₂).

In still further embodiments of the invention, the conversion step caninclude a blank subtraction step, combined data from two activereservoirs, and/or a smoothing step;

The blank subtraction step is used to subtract the blank signal from theactive signal in order to remove signal components that are not relatedto the analyte, thus obtaining a cleaner analyte signal. When raw signalis obtained from two active reservoirs the two raw signals can beaveraged or a summed value of the two raw signals can be used. In thesmoothing step, mathematical transformations are carried out whichindividually smooth signals obtained from the active and blankcollection reservoirs. These smoothing algorithms help improve thesignal-to-noise ratio in the biosensor, by allowing one to correct thesignal measurements obtained from the device to reduce unwanted noisewhile maintaining the actual signal sought.

More particularly, a blank subtraction step is used in the active-blankiontophoretic sampling system of the invention as follows. Signals fromthe blank (second) reservoir, taken at, or about the same time assignals from the active (first) reservoir, are used to substantiallyeliminate signal components from the active signal that are notspecifically related to the analyte. In this regard, the blank reservoircontains all of the same components as the active reservoir except forthe GOx enzyme, and the blank signal should thus exhibit similarelectrochemical current to the active signal, except for the signalassociated with the analyte. Accordingly, the following function can beused to subtract the blank signal from the active signal:Y _(t) =Y _(t,act) −d*Y _(t,blank)wherein: (Y_(t,act)) is the active signal (in nC) at time t;(Y_(t,blank)) is the blank signal (in nC) at time t; (Y_(t)) is the“blank subtracted” signal at time t; and (d) is the time-dependentfractional weight for the blank signal, and d preferably =1. In relationto the equation shown above that is used to subtract the blank signalfrom the active signal, when two active reservoirs are used d preferably=−1, or, more generally, as shown in the equation below, the summedsignal can be “weighted” to account for different contributions ofsignal from each reservoir.

In the case of two active reservoirs, each reservoir is capable ofgenerating raw signal and each contains all of the same components. Forexample, where two collection reservoirs are used for detecting glucoseboth reservoirs contain glucose oxidase. Accordingly, the followingfunction can be used:Y _(t,ε) =aY _(t,act1) +bY _(t,act2)wherein: “a” is the time-dependent fractional weight for the firstactive signal; (Y_(t,act1)) is the first active signal (in nC) at timet; “b” is the time-dependent fractional weight for the second activesignal; (Y_(t,act2)) is the second active signal (in nC) at time t;(Y_(t,ε)) is the summed signal at time t.

II. General Procedures for Smoothing Integrated Signals.

In the smoothing step, the active signal obtained from the first(active) reservoir can be smoothed using a smoothing function. Inmultiple active systems, the same smoothing can be applied to eachsignal before summing. In one embodiment, the function can be expressedas a recursive function as follows:E _(t,act) =w _(act) Y _(t,act)+(1−w _(act))(E _(t−1,act))wherein: (Y_(t,act)) is the measurement of the active signal (in nC) attime t; (E_(t,act)) is the estimate of the active signal (in nC) at timet for t>1 (at t=1, E_(t,act)=Y_(t,act)); and (w_(act)) is the “estimateweight” for the active biosensor, wherein 0≦w_(act)≦1.

The reference (blank) signal obtained from the second reservoir can alsobe smoothed using a similar recursive smoothing function. This functioncan be expressed as follows:E _(t,blank) =w _(blank) Y _(t,blank)(1−w _(blank))(E _(t−1,blank))wherein: (Y_(t,blank)) is the measurement of the blank signal (in nC) attime t; (E_(t,blank)) is the estimate of the blank signal (in nC) attime t for t>1 (at t=1, E_(t,blank)=Y_(t,blank)); and (w_(blank)) is the“estimate weight” for the blank biosensor, wherein 0≦w_(blank)≦1.

Once the active and blank signals have been individually smoothed, theblank signal can be subtracted from the active signal in order to obtaina signal that is indicative of the glucose reaction only. As discussedabove, the blank signal should exhibit a similar electrochemical currentto the active signal, except for the signal associated with the glucoseanalyte. In the practice of the invention, this blank subtraction stepcan subtract the value of the smoothed blank signal per se, or aweighted blank signal can be subtracted from the active signal, usingthe following function to obtain a fractional subtraction:E _(t) =E _(t,act) −d*E _(t,blank)wherein: (E_(t,act)) is the estimate of the active signal (in nC) attime t; (E_(t,blank)) is the estimate of the blank signal (in nC) attime t; (E_(t)) is the “blank subtracted” smoothed sensor signal at timet; and (d) is the time-dependent fractional weight for the blank signal.

The same recursive function can be used wherein the order of thesmoothing and blank subtraction steps are reversed such that:(Y_(t,act)) is the integral of the active signal (in nC) at time t;(Y_(t,blank)) is the integral of the blank signal (in nC) at time t;(Y_(t)) is the “blank subtracted” sensor signal (in nC) at time t; (d)is the time-dependent fractional weight for the blank signal; andY _(t) =Y _(t,act) −d*Y _(t,blank)E _(t) =wY _(t)+(1−w)(E _(t−1))

This smoothing can alternatively be carried out on discrete (nA) sensorsignals, with or without temperature and/or background subtractioncorrections. Smoothing can also be carried out on active signals or onaverages of two or more active signals. Further modifications to thesefunctions will occur to those of ordinary skill in the art, in light ofthe present enabling disclosure.

Step D: The Calibration Step.

Continuing with the method of the invention, any of the raw signalsobtained from Step A, the screened raw signal obtained from Step B, orthe initial output signal obtained from Step C (or from Steps B and C),can be converted into an analyte-specific value using a calibration stepwhich correlates the signal obtained from the sensing device with theconcentration of the analyte present in the biological system. A widevariety of calibration techniques can be used to interpret such signals.These calibration techniques apply mathematical, statistical and/orpattern recognition techniques to the problem of signal processing inchemical analyses, for example, using neural networks, genetic algorithmsignal processing, linear regression, multiple-linear regression,partial linear regression, deconvolution, or principal componentsanalysis of statistical (test) measurements.

One method of calibration involves estimation techniques. To calibratean instrument using estimation techniques, it is necessary to have a setof exemplary measurements with known concentrations referred to as thecalibration set (e.g., reference set). This set consists of m samples,each with n instrument variables contained in an m by n matrix (X), andan m by 1 vector (y), containing the concentrations. If a prioriinformation indicates the relationship between the measurement andconcentration is linear, the calibration will attempt to determine an nby 1 transformation or mapping (b), such thaty=Xbis an optimal estimate of y according to a predefined criteria. Numeroussuitable estimation techniques useful in the practice of the inventionare known in the art. These techniques can be used to provide constantparameters, which can then be used in a mathematical transformation toobtain a measurement value indicative of the concentration of analytepresent in the biological system at the times of measurement.

In one particular embodiment, the calibration step may be carried outusing artificial neural networks or genetic algorithms. The structure ofa particular neural network algorithm used in the practice of theinvention can vary widely; however, the network should contain an inputlayer, one or more hidden layers, and one output layer. Such networkscan be optimized on training data set, and then applied to a population.There are an infinite number of suitable network types, transferfunctions, training criteria, testing and application methods, whichwill occur to the ordinarily skilled artisan upon reading the instantspecification.

In the context of the iontophoretic glucose sampling device describedhereinabove (which can contain an active collection reservoir—with theGOx enzyme, and a blank collection reservoir; or alternately, two activereservoirs with the GOx enzyme), a preferred neural network algorithmwould use, for example, inputs selected from the following to provide ablood glucose measurement: elapsed time since calibration; signal fromthe active reservoir; signal from the blank reservoir; signal from twoactive reservoirs (either averaged or summed); calibration time;measured temperature; applied iontophoretic voltage; skin conductance;blood glucose concentration, determined by an independent means, at adefined calibration point; background; background referenced tocalibration; and, when operating in the training mode, measured glucose.

Whether or not the calibration step is carried out using conventionalstatistical techniques or neural network algorithms, the calibrationstep can include a universal calibration process, a single-pointcalibration process, or a multi-point calibration process. In oneembodiment of the invention, a universal calibration process is used,wherein the above mathematical techniques are used to derive acorrelation factor (or correlation algorithm) that allows for accurate,dependable quantification of analyte concentration by accounting forvarying backgrounds and signal interferences irrespective of theparticular biological system being monitored. In this regard, theuniversal calibrant is selected to provide a close correlation (i.e.,quantitative association) between a particular instrument response and aparticular analyte concentration, wherein the two variables arecorrelated.

In another embodiment, a single-point calibration is used. Moreparticularly, the single-point calibration process can be used tocalibrate measurements obtained by iontophoretic sampling methodologiesusing a reference measurement obtained by conventional (invasive)methods. Single-point calibration allows one to account for variablesthat are unique to the particular biological system being monitored, andthe particular sensing device that is being used. In this regard, thetransdermal sampling device is generally contacted with the biologicalsystem (placed on the surface of a subject's skin) upon waking. Afterthe device is put in place, it is preferable to wait a period of time inorder allow the device to begin normal operations.

Further, the sampling system can be pre-programmed to begin execution ofits signal measurements (or other functions) at a designated time. Oneapplication of this feature is to have the sampling system in contactwith a subject and to program the sampling system to begin sequenceexecution during the night so that it is available for calibrationimmediately upon waking. One advantage of this feature is that itremoves any need to wait for the sampling system to warm-up beforecalibrating it.

In the context of glucose monitoring, a blood sample can be extractedwhen the device has attained normal operations, such that the invasiveblood sample extraction is taken in a corresponding time period with ameasurement cycle. Actual blood glucose levels can then be determinedusing any conventional method (e.g., calorimetric, electrochemical,spectrophotometric, or the like) to analyze the extracted sample. Thisactual value is then used as a reference value in the single-pointcalibration process, wherein the actual value is compared against thecorresponding measured value obtained with the transdermal samplingdevice. In yet another embodiment, a multi-point calibration process isused, wherein the above-described single-point calibration process isrepeated at least once to provide a plurality of point calibrations. Forexample, the multi-point calibration process can be carried out atvarious time intervals over the course of a continual or continuousmeasuring period.

Continuing with the calibration step, the signals obtained from Step Band/or Step C, supra, can be subjected to further signal processingprior to calibration as follows. Referring particularly to the baselinebackground subtraction method of the conversion step (Step C), thecorrected signal should theoretically be directly proportional to theamount of analyte (glucose) present in the iontophoretically extractedsample. However, sometimes a non-zero intercept is obtained in thecorrelation between signal and reference glucose value. Accordingly, aconstant offset term (which can be positive or negative) is obtainedwhich can be added to the converted signal to account for a non-zerosignal at an estimated zero blood glucose concentration. The offset canbe added to the active sensor signal; or, in the case of aniontophoretic sampling system that obtains both active and blanksignals, the offset can be added to the blank-subtracted active signal.

The calibration step can be carried out using, for example, thesingle-point calibration method described hereinabove. The referenceblood glucose concentration thus obtained can then be used in thefollowing conversion factor:

$b_{gain} = \frac{{BG}_{cal} + \rho}{E_{cal} + {OS}}$wherein: (E_(cal)) is the blank-subtracted smoothed sensor signal (innC) at calibration; (BG_(cal)) is the reference blood glucoseconcentration (in mg/dL) at calibration; (b_(gain)) is the conversionfactor [(mg/dL)/nC]; (OS) is the offset calibration factor constant (innC) which can be calculated using standard regression analysis; and (ρ)is the calibration offset (in mg/dL). Post calibration data can then beconverted using the following function:EG _(t) =b _(gain) [E _(t) +OS]−ρwherein (EG_(t)) is the estimated blood glucose concentration (inmg/dL). Other signal values, such as Y_(t), can be substituted for E_(t)and E_(cal) depending upon the amount of prior signal processingperformed (see, e.g., Step C, supra).

Further signal processing can also be used to correct for time-dependentbehavior related to the particular sensor element that is used in thesensing operation. In this regard, signal measurements of certain types(such as the electrochemical signal measurements described herein)exhibit change over time for reasons which are not fully understood. Thepresent invention is not premised on any particular theory with respectto why such time-dependent change occurs. Rather, the inventionrecognizes that time-dependent behavior can occur, and corrects for thisbehavior using one or more mathematical functions.

Thus, in one embodiment, a corrected measurement can be calculated usinga mathematical function which compensates for time-dependent decline inthe biosensor signal between measurements during the period of continualor continuous measuring of the analyte concentration. The correctionfunction uses one or more additive decay parameters (α_(i)) and one ormore multiplicative decay parameters (ε_(i)), (both of which areempirically determined for the biosensor), and can be expressed asfollows:EG _(t) =b _(gain) [E _(t)(1+ε_(i) t)+OS]+α _(i) t−ρwherein:

$b_{gain} = \frac{{BG}_{cal} + \rho - {\alpha_{i}t_{cal}}}{{E_{cal}\left( {1 + {\varepsilon_{i}t_{cal}}} \right)} + {OS}}$and (t_(cal)) is the calibration point; (EG_(t)) is the estimated bloodglucose concentration at-time t; (E_(t)) is the analyte signal at timet; (OS) is the constant offset term which accounts for a non-zero signalat an estimated zero blood glucose concentration (as described above);(ε) is a gain term for time-dependent signal decline and can havemultiple time segments (e.g., i=1, 2, or 3); (α) is a correction termfor a linear time-dependent signal decline in the time segments and canhave multiple time segments (e.g., i=1, 2, or 3); (t) is the elapsedtime, and (ρ) is the calibration offset (in mg/dl).

In an alternative embodiment, a corrected measurement can be calculatedusing a mathematical function which compensates for time-dependentdecline in the biosensor signal between measurements, during the periodof continual or continuous measuring of the analyte concentration, bycorrelating signal at the beginning of the measurement series to a unitof decay. The correction function uses an additive decay parameter (α)and a decay correction factor (γ). This equation allows a time-dependentmultiplicative correction to be applied to the integrated signal in amanner that amplifies, to a greater extent, those signals that have beenobserved to decay at a greater rate (e.g., empirically, signals thatgive lower BGain tend to decay faster). Use of the BGAIN factor, asdescribed herein, can insure that a reasonable calibration factor isobtained.

In this embodiment, EG_(t), the calculated value of blood glucose at themeasurement time, is computed as follows:

${EG}_{t} = {{\left( {\left\lbrack {\frac{{BG}_{cal} + {\alpha\; t_{cal}}}{E_{cal} + {OS}} - {\gamma\; t_{cal}}} \right\rbrack + {\gamma\; t}} \right)*\left( {E_{t} + {OS}} \right)} + {\alpha\; t}}$${{where}\mspace{14mu}{BGAIN}} = \left\lbrack {\frac{{BG}_{cal} + {\alpha\; t_{cal}}}{E_{cal} + {OS}} - {\gamma\; t_{cal}}} \right\rbrack$wherein: BG_(cal) is the true blood glucose at the calibration point;E_(cal) is the analyte signal at calibration; (t_(cal)) is the elapsedtime of the calibration point; (EG_(t)) is the estimated blood glucoseconcentration at time t; (E_(t)) is the analyte signal at time t; (OS)is the constant offset term which accounts for a non-zero signal at anestimated zero blood glucose concentration (as described above); (γ) isa time-dependent correction term for signal decline; (α) is atime-dependent correction term for signal decline; and (t) is theelapsed time.

Employing these equations a “time segmentation” can be performed asfollows:

$\begin{matrix}{{{BGAIN}_{1} = \left\lbrack {\frac{{BG}_{cal} - {\alpha_{1}t_{c\;{al}}}}{E_{{ca}\; l} + {OS}} - {\gamma_{1}t_{{ca}\; l}}} \right\rbrack}\mspace{14mu}\mspace{124mu}{{{if}\mspace{20mu} t} < t_{12}}} \\{{{BGAIN}_{2} = \left\lbrack {\frac{{BG}_{cal} - {\alpha_{1}t_{12}} - {\alpha_{2}\left( {t_{c\;{al}} - t_{12}} \right)}}{E_{{ca}\; l} + {OS}} - {\gamma_{1}t_{12}} - {\gamma_{2}\left( {t_{c\;{al}} - t_{12}} \right)}} \right\rbrack}\mspace{121mu}{{{if}\mspace{14mu} t_{12}} < t_{c\;{al}} < t_{23}}} \\{{BGAIN}_{3} = \left\lbrack {\frac{{BG}_{c\;{al}} - {\alpha_{1}t_{12}} - {{\alpha 2}\left( {t_{{ca}\; l} - t_{12}} \right)} - {{\alpha 3}\left( {t_{c\;{al}} - t_{23}} \right)}}{E_{c\;{al}} + {OS}} -} \right.} \\{\left. {{\gamma_{1}t_{12}} - {\gamma_{2}\left( {t_{{ca}\; l} - t_{12}} \right)} - {\gamma_{3}\left( {t_{{ca}\; l} - t_{23}} \right)}} \right\rbrack{{{if}\mspace{14mu} t_{23}} < t_{{ca}\; l}}}\end{matrix}$EG _(t)=(BGAIN ₁+γ₁ t)*(E _(t) +OS)+α₁ tif t<t ₁₂EG _(t)=(BGAIN ₂+γ₁ t ₁₂+γ₂(t−t ₁₂))*(E _(t) +OS)+α₁ t ₁₂+α₂(t−t ₁₂)if t ₁₂<t<t₂₃

EG_(t) = (BGAIN₃ + γ₁t₁₂ + γ₂(t₂₃ − t₁₂) + γ₃(t − t₂₃)) * (E_(t) + OS) + α₁t₁₂ + α₂(t₂₃ − t₁₂) + α₃(t − t₂₃)if  t₂₃ < twherein: EG_(t) is the calculated value of blood glucose at themeasurement time; BG_(cal) is the true blood glucose at the calibrationpoint, t is the elapsed time (hence t_(cal) is the elapsed time at thecalibration point), OS is the offset parameter, α_(i) and γ_(i) are thetime dependent correction terms to account for the declining signal withtime. To avoid a dominant time correction term as the elapsed timeincreases, the time correction parameters α_(i) and γ_(i) are distinctfor three different time intervals (“i”): 0 to 6 hours (e.g., i=1), 6 to10 hours (e.g., i=2), and 10 to 14 hours (e.g., i=3), as shown above.Therefore, t₁₂=6 hours and t₂₃=10 hours.

The time segmentation allows for greater flexibility in predictingnon-linear signal decay terms.

The signal processing methods and techniques described in Steps Athrough D can be combined in a variety of ways to provide for improvedsignal processing during analyte measurement. In one embodiment, anactive/blank sampling system is used to obtain the raw signal in Step A.These raw signals are then screened in Step B to obtain screened data.These screened data are then subjected to a temperature correction usingthe K1 temperature correction, and then converted using the baselinesubtraction and integration methods of Step C. The converted data arealso smoothed (both active and blank) using the smoothing functions ofStep C, the smoothed data are temperature corrected using the K2temperature correction, and a blank subtraction is carried out. Thesmoothed and corrected data are then converted to the analyteconcentration in the biological system using the calibration methods ofStep D to perform a single-point calibration, wherein the data is alsorefined using the offset and time-dependent behavior corrections toobtain a highly accurate analyte concentration value.

In another embodiment, if two active reservoirs (A₁/A₂) are used, a“sensor consistency check” can be employed that detects whether thesignals from the reservoirs are changing in concert with one another.This check compares the percentage change from the calibration signalfor each reservoir, then calculates the difference in percentage changein signal between the two reservoirs. If this difference is greater thansome threshold, then the signals are not “tracking” one another and thisdata point can be screened as in Step B. This check verifies consistencybetween the two sensors. A large difference can indicate noise in thesignals.

In yet another embodiment of the present invention a “Calibration FactorCheck” may be employed. This check provides control over unreasonablefinger prick measurements or incorrect entries and provides additionalassurance that a reasonable calibration slope has been generated.Typically, there are two calibration factors that are calculated atcalibration: BGAIN and CAL RATIO. If BGAIN is less than or equal to apredetermined threshold value, or if the CAL RATIO is greater than orequal to a predetermined threshold value, then a calibration error isindicated. Such an error can be displayed to the user, for example, acalibration window can appear on the monitor's display appear. Such anerror indicates to the users that the user must perform the calibrationagain. For the Calibration Factor Check, CAL RATIO can be calculated asfollows:

${CALRATIO} = \left\lbrack \frac{{BG}_{c\;{al}}}{E_{c\;{al}} + {OS}} \right\rbrack$wherein, BG_(cal) is the true blood glucose at the calibration point;E_(cal) is the analyte signal at calibration; and (OS) is the constantoffset term which accounts for a non-zero signal at an estimated zeroblood glucose concentration.Step E: Time Forecasting Measurements.

The corrected analyte value obtained using the above techniques can beused to predict future (e.g., time forecasting) or past (e.g.,calibration) target analyte concentrations in the biological system. Inone embodiment, a series of analyte values are obtained by performingany combination of Steps A, B, C, and/or D, supra, in an iterativemanner. These measurements are then used to predict unmeasured analytevalues at different points in time, future or past.

More particularly, the above-described iontophoretic sampling process iscarried out in order to obtain three or more measurements of the targetanalyte. Using these measurements, an additional measurement can becalculated. The additional measurement is preferably calculated using aseries function.

In the context of blood glucose monitoring, it has been found that theactual (real-time) glucose level in a subject differs from the measuredglucose level obtained using a sampling device that extracts glucosefrom the subject using iontophoresis. The difference is due, in part, toa lag time between extracting the glucose analyte and obtaining ameasurement from the extracted glucose. This lag time can vary dependingon factors such as the particular subject using the device, theparticular area of skin from which glucose is extracted, the type ofcollection reservoir used, and the amount of current applied. In orderto compensate for this inherent lag time, the method of the presentinvention can utilize data obtained from previous measurements and amathematical function in order to predict what a future analyteconcentration will be. In this case, the predicted future reading can beused as a “real-time value” of the analyte level.

In another embodiment, mathematical methods can be used to predict pastmeasurements, such as in the context of making a calibration. Moreparticularly, measurements obtained using the above-describedtransdermal sampling device can be calibrated against one or morereference measurements obtained by conventional (blood extraction)methods. In such calibration processes, actual blood glucose levels aredetermined using conventional analytical methods (e.g., calorimetric,electrochemical, spectrophotometric, or the like) to analyze anextracted blood sample. These actual measurements are then compared withcorresponding measurements obtained with the transdermal samplingdevice, and a conversion factor is then determined. In normaloperations, the transdermal sampling device is generally first contactedwith the biological system (placed on the surface of a subject's skin)upon waking. After the device is put in place, it is preferable to waita period of time in order allow the device to attain normal operatingparameters, after which time the device can be calibrated. However, if ablood sample is extracted at the time when the device is first applied(as would normally be most convenient), there may not be a correspondingsignal from the transdermal sampling system which can be compared withthe reference value obtained from the extracted blood sample. Thisproblem can be overcome using prediction methods which allow one toperform a conventional blood glucose test (via a blood sampleextraction) when the device is first applied, and then calibrate thedevice at a later time against the results of the conventional glucosetest.

A number of mathematical methods for predicting future or pastmeasurements can be used in the practice of the invention. For example,linear or nonlinear regression analyses, time series analyses, or neuralnetworks can be used to predict such measurements. However, it ispreferred that a novel combination of exponential smoothing and a Taylorseries analysis be used herein to predict the future or pastmeasurement.

A number of other physiological variables may be predicted using theabove techniques. For example, these prediction methods can be used totime forecast those physiological variables that cannot be measured inreal-time, or that demonstrate frequent fluctuations in their data.Examples of physiological functions and the variables that characterizethem include, but are not limited to, cerebral blood flow (in thetreatment of stroke patients) which is related to blood viscosity andthe concentrations of plasma proteins and clotting factors in the bloodstream (Hachinski, V. and Norris, J. W., “The Acute Stroke,”Philadelphia, F A Davis, 1985); pulmonary function (in asthma patients)as measured by lung volumes in the different phases of respiration(Thurlbeck, W. M. (1990) Clin. Chest Med. 11:389); and heart activity(in recurrent cardiac arrest) as measured by electrical activity of theheart (Marriott, H J L, “Practical Electrocardiography”, 8th Ed.,Baltimore, Williams & Wilkins, 1983). Other examples of physiologicalvariables that can be predicted, include renal dialysis, where bloodconcentrations of urea and blood gases are followed (Warnock, D. G.(1988) Kidney Int. 34:278); and anesthesia treatment, where variousparameters (e.g., heart rate, blood pressure, blood concentration of theanesthesia) are monitored to determine when the anesthesia will stopfunctioning (Vender, J. S., and Gilbert, H. C., “Monitoring theAnesthetized Patient,” in Clinical Anesthesia, 3rd Ed., by Barash etal., Lippincott-Raven Publishers, Philadelphia, 1996).

Step F: Controlling a Physiological Effect.

The analyte value obtained using the above techniques can also be usedto control an aspect of the biological system. e.g., a physiologicaleffect. In one embodiment, an analyte value obtained as described aboveis used to determine when, and at what level, a constituent should beadded to the biological system in order to control the concentration ofthe target analyte.

More particularly, in the context of blood glucose monitoring, use ofprediction techniques (Step E, supra) allows for accurate predictions ofeither real-time or future blood glucose values. This is of particularvalue in predicting hypoglycemic episodes which can lead to diabeticshock, or even coma. Having a series of measurements obtained from thecontinual iontophoretic sampling device, and the capability to predictfuture values, allows a subject to detect blood glucose swings or trendsindicative of hypoglycemic or hyperglycemic episodes prior to theirreaching a critical level, and to compensate therefor by way ofexercise, diet or insulin administration.

A feedback control application of the present invention entails using afunction to predict real-time blood glucose levels, or measurementvalues of blood glucose levels at a different time, and then the same tocontrol a pump for insulin delivery to treat hyperglycemia.

EXAMPLES

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the devices, methods, and formulae of the presentinvention, and are not intended to limit the scope of what the inventorsregard as their invention. Efforts have been made to ensure accuracywith respect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

Example 1 Signal Processing for Measurement of Blood Glucose

In order to assess the signal processing methods of the presentinvention, an iontophoretic sampling device was used to extract a seriesof 525 blood glucose samples from an experimental population of humansubjects, and non-processed measurement values were compared againstmeasurement values obtained using the data screening and correctionalgorithm of the present invention.

More particularly, iontophoretic sampling was performed on subjectsusing a GlucoWatch™ (Cygnus, Inc., Redwood City, Calif.) iontophoreticsampling system. This transdermal sampling device, which is designed tobe worn like a wrist watch, uses iontophoresis (electroosmosis) toextract glucose analyte into a collection pad worn beneath the watch.Glucose collected into the GlucoWatch™ sampling system triggers anelectrochemical reaction with a reagent in the pad, giving rise to acurrent which is sensed, measured, and converted to a blood glucoseconcentration. Measurements are taken on a continual basis, whereincombined extraction and sensing (measurement cycles) were set at 30minutes. Iontophoresis was carried out using two collection padscontacted with Ag/AgCl iontophoretic electrodes, an iontophoreticcurrent density of 0.3 mA/cm², and the electrical polarity of theelectrodes was switched halfway through the 30 minute measurement cycle.Sensing was carried out using platinum-based biosensor electrodes whichwere contacted with the collection pads. A description of theGlucoWatch™ sampling system can be found in publication to Conn, T. E.(Jan. 15, 1997) “Evaluation of a Non-Invasive Glucose Monitoring Systemfor People with Diabetes,” given at the Institute of Electrical andElectronics Engineers (IEEE) meeting entitled “Engineering in Medicine &Biology,” Stanford, Calif., which publication is incorporated herein byreference.

Concurrent with obtaining the calculated blood glucose values (from theGlucoWatch™ sampling system), blood samples (finger sticks) wereobtained and analyzed for use as reference measurements. As a result,525 sets of paired measurements (reference and calculated measurements)were obtained. A comparison was then made between the referencemeasurements and the calculated measurements (either raw, or signalprocessed using the methods of the invention). Two different sets ofdata screens were used as follows: (a) maximum temperature change overtime (d(temp)/d(time)), perspiration threshold, and a thresholddeparture from monotonicity (this set of temperature screens isindicated as (+) in Table 1 below); or (b) maximum temperature changeover time (d(temp)/d(time)), perspiration threshold, a thresholddeparture from monotonicity, and a threshold baseline background changeover time (this set of temperature screens is indicated as (++) in Table1 below). The correction algorithm that was used is as follows:EG _(t) =b _(gain) [E _(t)(1+ε_(i) t)+OS]+α _(i) t−ρwherein:

$b_{gain} = \frac{{BG}_{c\;{al}} + \rho - {\alpha_{c\;{al}}t}}{{E_{c\;{al}}\left( {1 + {\varepsilon_{i}t_{c\;{al}}}} \right)} + {OS}}$and (t_(cal)) is the calibration point; (EG_(t)) is the estimated bloodglucose concentration at time t; (E_(t)) is the analyte signal at timet; (OS) is the constant offset term which accounts for a non-zero signalat an estimated zero blood glucose concentration (as described above);(ε) is a gain term for time-dependent signal decline and can havemultiple time segments (e.g., i=1, 2, or 3); (α) is a correction termfor a linear time-dependent signal decline in the time segments and canhave multiple time segments (e.g., i=1, 2, or 3); (t) is the elapsedtime, and (ρ) is the calibration offset (in mg/dl).

In the comparison, an Error Grid Analysis (Clarke et al. (1987) DiabetesCare 10:622-628) was used to assess device effectiveness, whereincalculated measurements were plotted against the corresponding referencemeasurements. An effective blood glucose monitoring device should havegreater than approximately 85-90% of the data in the A and B regions ofthe Error Grid Analysis, with a majority of the data in the A region(Clark et al., supra). The results of the Error Grid Analysis arepresented below in Table 1 as (A+B %). As can be seen, the combinationof data screening methods and the correction algorithm of the presentinvention met this effective criteria.

Another measure of device accuracy is the mean absolute % error (MPE(%))which is determined from the mean of individual % error (PE) given bythe following function:

${PE} = \frac{{EG}_{t} - {BG}_{t}}{{BG}_{t}}$wherein BG_(t) is the reference glucose measurement and EG_(t) is thecalculated glucose measurement. Effective measurements should have aMPE(%) of about 25% or less. The results of the MPE(%) are also depictedin Table 1. As can be seen, the combination of data screening methodsand the correction algorithm of the present invention met this effectivecriteria.

The correlation between calculated and measured blood glucose values wasalso assessed. The correlation coefficient values (R) are also presentedin Table 1 below. Effective measurements should have R values of greaterthan about 0.85. As can be seen, the combination of data screeningmethods and the correction algorithm of the present invention providefor increased correlation between actual and measured values.

TABLE 1 525 Total Paired Data Algorithm Screen No. pts. MPE (%) A + B(%) Other (%) R 0 0 525 54 73 27 0.54 + + 467 24 90 10 0.87 + ++ 308 2091 9 0.90

1. One or more microprocessors comprising programming to controloperating a sensing device to obtain two or more active signals, wherein(i) said sensing device is in operative contact with an analyte, (ii)said sensing device obtains an active signal from the analyte, and (iii)said active signal is specifically related to analyte amount orconcentration present in a biological system; assigning a fractionalweight to each of said two or more active signals based on when therespective active signals were generated with respect to one another;determining a weighted signal that accounts for the differentcontributions of each of said two or more active signals and theirassociated fractional weights; and performing a calibration step thatconverts the weighted signal to a measurement value indicative of theamount or concentration of analyte present in the biological system. 2.The one or more microprocessors of claim 1, wherein said one or moremicroprocessors further comprise programming to control operating asampling device for extracting the analyte from the biological system,wherein said sampling device is adapted for extracting the analyteacross a skin or mucosal surface of said biological system.
 3. Amonitoring system for measuring an analyte present in a biologicalsystem, said system comprising, in operative combination: the one ormore microprocessors of claim 1; and the sensing device.
 4. A monitoringsystem for measuring an analyte present in a biological system, saidsystem comprising, in operative combination: the one or moremicroprocessors of claim 2; the sampling device; and the sensing device.5. The monitoring system of claim 4, wherein the sampling devicecomprises one or more collection reservoirs for containing the extractedanalyte.
 6. The monitoring system of claim 5, wherein one or morecollection reservoirs comprise an enzyme that reacts with the extractedanalyte to produce an electrochemically detectable signal.
 7. Themonitoring system of claim 6, wherein the analyte is glucose and theenzyme comprises glucose oxidase.
 8. The monitoring system of claim 3,wherein the analyte is glucose.
 9. The monitoring system of claim 4,wherein the analyte is glucose.
 10. One or more microprocessorscomprising Programming to control operating a sensing device to obtaintwo or more active signals, wherein (i) said sensing device is inoperative contact with an analyte, (ii) said sensing device obtains anactive signal from the analyte, and (iii) said active signal isspecifically related to analyte amount or concentration present in abiological system: assigning a fractional weight to each of said two ormore active signals; determining a weighted signal that accounts for thedifferent contributions of each of said two or more active signals andtheir associated fractional weights: and performing a calibration stepthat converts the weighted signal to a measurement value indicative ofthe amount or concentration of analyte present in the biological system.wherein the sensing device uses an iontophoretic current to extract theanalyte from the biological system.
 11. A method for measuring ananalyte amount or concentration present in a biological system, saidmethod comprising: obtaining an active signal from the analyte, whereinsaid active signal is related to analyte concentration; repeating saidobtaining to provide two or more active signals; assigning a fractionalweight to each of said two or more active signals based on when therespective active signals were generated with respect to one another;determining a weighted signal that accounts for the differentcontributions of each of said two or more active signals and theirassociated fractional weights; and performing a calibration step whichconverts the weighted signal to a measurement value indicative of theamount or concentration of analyte present in the biological system. 12.The method of claim 11, wherein said weighted signal is a weighted sumof each of said two or more active signals.
 13. The method of claim 11,wherein said weighted signal is a weighted average of each of said twoor more active signals.
 14. The method of claim 11, wherein the analyteis extracted from the biological system into a first collectionreservoir to obtain an active signal in said reservoir.
 15. The methodof claim 14, wherein the analyte is extracted from the biological systemalternatively into (i) the first collection reservoir to obtain anactive signal in said first collection reservoir, and (ii) a secondcollection reservoir to obtain an active signal in said secondcollection reservoir.
 16. The method of claim 15, wherein said weightedsignal is a weighted sum of the two or more active signals.
 17. Themethod of claim 16, wherein said weighted signal is determined asfollows: Y_(t,ε)=aY_(t1,act1)+bY_(t2,act2′)wherein Y_(t,ε)is the summedsignal at time t, a is a fractional weight for a first signal at time t1(Y_(t1,act1)), and b is the fractional weight for a second active signalat time t2 (Y_(t2,act2)).
 18. The method of claim 15, wherein saidweighted signal is a weighted average of the two or more active signals.19. A method for measuring an analyte amount or concentration present ina biological system, said method comprising: obtaining an active signalfrom the analyte, wherein said active signal is related to analyteconcentration: repeating said obtaining to provide two or more activesignals; assigning a fractional weight to each of said two or moreactive signals; determining a weighted signal that accounts for thedifferent contributions of each of said two or more active signals andtheir associated fractional weights; and performing a calibration stepwhich converts the weighted signal to a measurement value indicative ofthe amount or concentration of analyte present in the biological system,wherein the first collection reservoir is in contact with the skin ormucosal surface of the biological system and the analyte is extractedusing an iontophoretic current applied to said skin or mucosal surface.20. The method of claim 14, wherein the first collection reservoircontains an enzyme that reacts with the extracted analyte to produce anelectrochemically detectable signal.
 21. The method of claim 20, whereinthe analyte is glucose and the enzyme is glucose oxidase.
 22. The methodof claim 11, wherein the analyte is glucose.
 23. A method for measuringan analyte amount or concentration present in a biological system, saidmethod comprising: obtaining an active signal from the analyte, whereinsaid active signal is related to analyte concentration; repeating saidobtaining to provide two or more active signals; assigning a fractionalweight to each of said two or more active signals; determining aweighted signal that accounts for the different contributions of each ofsaid two or more active signals and their associated fractional weights;and performing a calibration step which converts the weighted signal toa measurement value indicative of the amount or concentration of analytepresent in the biological system, wherein obtaining the active signalcomprises a baseline background subtraction method to remove backgroundnoise from the active signal.
 24. The method of claim 15, wherein thesecond collection reservoir comprises an enzyme that reacts with theextracted analyte to produce an electrochemically detectable signal. 25.The method of claim 11, wherein an initial signal output is integratedover a sensing time period to provide the active signal.
 26. A methodfor measuring an analyte amount or concentration present in a biologicalsystem, said method comprising: obtaining an active signal from theanalyte, wherein said active signal is related to analyte concentration;repeating said obtaining to provide two or more active signals;assigning a fractional weight to each of said two or more activesignals; determining a weighted signal that accounts for the differentcontributions of each of said two or more active signals and theirassociated fractional weights; and performing a calibration whichconverts the weighted signal to a measurement value indicative of theamount or concentration of analyte present in the biological system,wherein the calibration entails the use of a neural network algorithmthat correlates each weighted signal with a measurement value indicativeof the concentration of analyte present in the biological system. 27.The method of claim 11, wherein said fractional weights aretime-dependent fractional weights.
 28. The method of claim 11, whereineach active signal is subjected to a data screen which invalidates orcorrects poor or incorrect signals based on a detected parameterindicative of a poor or incorrect signal.
 29. The method of claim 11,wherein the calibration step entails a single-point calibration againsta calibration reference value.
 30. The method of claim 11, furthercomprising: transdermally extracting the analyte from the biologicalsystem using a sampling device that is in operative contact with a skinor mucosal surface of said biological system before said obtaining; andrepeating said extracting and said obtaining to provide two or moreactive signals.